Cloud Blog

Chrome Enterprise brings more protections to healthcare technologies

Thu, 12 Nov 2026 18:55:00 +0000

Healthcare is adopting AI quickly, and it's moving beyond chat. Clinical teams are starting to use AI agents that draft documentation, summarize charts, navigate web apps, and complete multi-step tasks on a clinician's behalf. Many of these agents work in the same place—the browser. As electronic health records (EHRs) and clinical apps move to the web, the browser is where clinicians, applications, patient data, and AI services meet. When clinicians or agents act inside a session, security teams need clear control over what they can access.

Healthcare systems frequently adopt a multi-vendor AI strategy, a flexibility that should be fully supported. Whether clinicians are using Gemini™ or an array of other web-based AI tools and extensions, Chrome Enterprise Premium secures data across the board to:

Guard sensitive data: Advanced DLP controls restrict unauthorized copying and pasting, form submission, printing, and screen capture of sensitive data—preventing patient data from leaking into unapproved AI prompts or browser extensions.
Maintain full operational visibility: Granular reporting and visibility into browser activity, extension usage, and data transfers help security teams uncover shadow AI and maintain internal oversight.
Govern extensions and web AIs consistently: Security policies and extension controls apply directly at the browser layer, so IT teams can manage permissions, block risky extensions, and enforce guardrails across every AI service without needing a separate harness for each tool.

Since we introduced new healthcare integrations in May, Chrome Enterprise has continued to invest in the healthcare ecosystem. Today we're sharing the next set of milestones that bring Chrome Enterprise to more clinical endpoints.

Chrome Enterprise is now compatible with IGEL® OS

As healthcare organizations accelerate the adoption of web-based clinical applications and AI-powered workflows, securing both the browser and the endpoint has become increasingly important. To address this, Chrome Enterprise is now compatible with IGEL® OS and soon available in the IGEL® Marketplace, allowing healthcare IT to easily deploy and manage the browser across IGEL OS™ endpoints, including shared workstations, nursing stations, and clinical carts.

This collaboration combines Google's enterprise-grade browser management and security capabilities with the IGEL Adaptive Secure Endpoint Platform,™ providing an immutable, centrally-managed endpoint foundation designed to reduce the attack surface and simplify IT operations. Through the IGEL App Portal and IGEL Universal Management Suite (UMS), organizations can streamline application deployment and endpoint governance, while the Google Admin console provides centralized browser policy and extension management.

Native browsing for flexible clinical endpoints with Citrix® UniconOS

Through our expanded collaboration with Citrix, healthcare systems can now run Chrome Enterprise natively on Citrix UniconOS endpoints. Clinicians get a fast, familiar experience on any device, while IT gains another flexible option for adding secure access and data protections across their clinical environments.

Expanding collaboration with Omnissa® for enhanced device trust

Many healthcare organizations use Omnissa Workspace ONE® and Omnissa Horizon® to manage, secure and deliver critical clinical applications. We're expanding our collaboration with Omnissa to bring zero-trust security and device posture assessment to healthcare environments. Building on May's integration, organizations can verify device health before clinicians reach critical applications.

Optimizing Epic® Workflows at the point of care

Beyond endpoint expansion, we are introducing new Chrome Enterprise capabilities that streamline clinic workflows, including support for silent printing and PDF updates for Epic® Hyperspace® for the web. This allows clinicians to route essential orders and wristbands directly to authorized printers, while native PDF rendering keeps protected health information strictly isolated within the active, managed session.

Scaling clinical technology with confidence

Together, these updates give IT and security teams the confidence to say yes to modern clinical technology, supporting the patient care experience—without giving up critical protections and control. As healthcare organizations continue deploying enterprise AI solutions like Gemini or managing a fast-growing ecosystem of third-party AI web apps and browser extensions, Chrome Enterprise provides the unified security, Data Loss Prevention (DLP), and governance layer needed to empower care teams with confidence.

To get started, explore Chrome Enterprise in the IGEL Marketplace, see how our latest updates support your Epic® and clinical workflows or visit our website or contact us today.

Chrome Enterprise Premium features and partner integrations are subject to change. Epic and Hyperspace are registered trademarks of Epic Systems Corporation.

Powering agentic cloud governance: What's new in Organization Policy

Thu, 12 Nov 2026 17:00:00 +0000

Even as sophisticated, new zero-day exploits continue to emerge, most cybersecurity incidents still start from the same common sources: an attacker locating and exploiting a fundamental, easily preventable cloud misconfiguration.

Organization Policy Service can help you secure resource configurations and set up security guardrails across your Google Cloud resource hierarchy. Using a catalog of over 100 predefined policies for many common cloud governance needs, as well as Custom Organization Policy, you can more easily set up boundaries with granular control over resources.

To help platform and security teams author, audit, and scale guardrails with greater speed and simplicity, we are introducing the general availability of our Organization Policy Model Context Protocol (MCP) server and natural-language constraint authoring powered by Gemini.

Managing Organization Policy with agents

Rather than forcing administrators to manually parse complex hierarchy chains, the Organization Policy MCP server offers tools that allow autonomous agents and pipelines to interact directly with Organization Policy.

Using the Organization Policy MCP server, agents can assist administrators with posture audits, drift detection, and exemption management. Key capabilities include:

Constraint discovery: Tools like 'ListConstraints' and 'GetCustomConstraint' allow agents to understand the specific guardrails that apply to a project, folder, or organization.
Policy evaluation: The 'GetEffectivePolicy' tool calculates the effective policy after considering hierarchy inheritance and tag-based conditions. You can use this to verify if a configuration change will be compliant.
Manage policy: 'CreatePolicy' and 'CreateCustomConstraint' allow platform teams to manage guardrails via conversational workflows.

For example, you can combine your preferred coding agent with the Organization Policy MCP tools to create a custom dashboard to visualize the constraints applied to an organization and identify the folders and projects in the organization that have been exempted from the policy. You can also build custom agents and skills to streamline managing Organization Policy.

Figure 1: Custom dashboard created with Organization Policy MCP tools displaying enforced constraints

Figure 2: Visualize enforced constraints and identify exemptions with Organization Policy MCP tools.

Simplifying custom constraint authoring with Gemini

While the Organization Policy constraint catalog provides built-in managed constraints for common use cases, each organization has its own unique architectural standards. Custom Organization Policy enables customers to express these standards as a custom constraint and enforce them across supported services.

Traditionally, administrators would write these rules in common expression language (CEL), which required a sharp understanding of Google Cloud resource schemas. Today, administrators can author custom Organization Policy in natural language through a Gemini prompt.

For example, you can prompt Gemini to require the use of agent-identity in Agent Runtime, to require that all agents deployed to Agent Runtime use an approved Agent Gateway, and to prevent users from disabling auto-upgrades on GKE, and refine custom constraints conversationally.

To help ensure safe deployments, Gemini can also generate compliant and non-compliant test resource configurations that can help you test policy and validate in dry-run mode before enforcing the constraint.

Figure 3: Define custom constraints with a natural language prompt using Gemini

For targeted examples, the sample custom constraints library from Google Cloud Professional Services features pre-authored custom constraint samples based on CIS and PCI standards. Custom constraints are supported on a wide range of Google Cloud services including core AI and compute services including Agent Runtime, Agent Search, and Agent Studio on Gemini platform, Cloud Run, and Google Kubernetes Engine.

Build faster, with confidence

Organization Policy provides proactive cloud governance of resource configurations on Google Cloud. It lets administrators enforce critical security guardrails so engineering teams can build and deploy quickly and securely.

With a growing catalog of built-in guardrails, autonomous tools for administrators, and the ability to tailor custom constraints across Google Cloud services, Organization Policy gives enterprises the control they need to scale safely.

Explore the Organization Policy documentation to learn more about resource governance in Google Cloud and set up your foundational guardrails.

Simplifying last mile cloud connectivity with Lumen and partner Cross-Cloud Interconnect

Thu, 12 Nov 2026 17:00:00 +0000

Cross-Cloud Interconnect is expanding to connect to more of your workloads. We designed Google Cloud's Cross-Cloud Network to simplify and optimize how organizations connect and secure applications across cloud and on-premises environments. Last year, we launched partner Cross-Cloud Interconnect for AWS based on an open specification, turning complex cross-cloud network builds into a managed service that provisions in minutes.

Today, we are extending partner Cross-Cloud Interconnect for last mile, starting in preview with Lumen Technologies, to bring that same simplicity and high-bandwidth connectivity to private clouds, on-premises data centers, neoclouds, and other clouds with expanded reach.

“The network needs to become as dynamic and easy to consume as the cloud itself. By bringing Google Cloud and Lumen connectivity together, we’re making it simpler for enterprises to connect their locations to the cloud with the performance and reliability they need. This is an important step toward a more programmable, cloud-like networking experience.” - Jim Fowler, EVP, Chief Technology & Product Officer, Lumen Technologies

Enterprise workloads and AI pipelines run across an increasing mix of public clouds, specialized neoclouds, private clouds, and on-premises sites. AI agents are also starting to assist with data and operational technology at customer branches, retail stores, and factory floors. Moving data reliably across these environments requires high-capacity private connectivity, but provisioning and managing that connectivity still relies on separate tools, providers, and operational models.

Cloud compute and storage capacity spin up in seconds, while connecting a physical site or private cloud can take weeks or months of manual quoting, cross-connect ordering, and routing configuration. Building solutions for high availability adds even more friction, as network engineers have to coordinate dual circuits across separate facilities, configure BGP failover, and manage encryption keys manually. With partner Cross-Cloud Interconnect for last mile and Lumen Technologies, you can leverage Lumen's expansive last-mile network to establish dedicated Layer 3 private connectivity to Google Cloud in minutes instead of months.

Programmable, resilient cloud connectivity

Partner Cross-Cloud Interconnect uses the open interconnect API specifications and provisioning process, so network teams can use one operational runbook for private cloud, neocloud, on-premises, and multicloud connections.

On-demand connectivity to any environment: 1 to 50 Gbps scaling in minutes across public clouds, private clouds, AI neoclouds, and on-premises locations
Resilient and secure by default: pre-provisioned capacity across redundant routers in separate facilities/EADs, coordinated maintenance, and wire-speed MACsec encryption
Lower total cost of ownership (TCO): predictable flat-rate bandwidth tiers inclusive of Google Cloud data egress, eliminating idle physical port capacity and cross-connect fees

Choose the option that fits your needs

Partner Cross-Cloud Interconnect for CSPs: best for flexible, automated multicloud connectivity

Connect Google Cloud to providers like AWS or OCI without ordering dedicated physical ports or managing cross-connects

Partner Cross-Cloud Interconnect for last mile: best for expanding reach to private clouds, neoclouds, on-premises sites, and multicloud environments

Leverage partner last-mile networks, starting with Lumen, to connect your private clouds, neoclouds, data centers, and other cloud locations without waiting on long provisioning lead times

Availability

Partner Cross-Cloud Interconnect for last mile is available in preview in us-east4 (Northern Virginia) with Lumen Technologies, with additional regions coming soon.

To join in the preview, express your interest via this form, or contact your Google Cloud or Lumen account team. You can also read the Lumen announcement to learn more.

What’s new with Google Data Cloud

Thu, 12 Nov 2026 17:00:00 +0000

November 9 - November 13

Bringing conversational analytics to Gemini with Data Cloud connectors: Data Cloud connectors in Gemini are now in preview, enabling knowledge workers to query live enterprise data using natural language directly within tools like Google Docs, Gmail, and Google Slides. The capability connects across BigQuery, Looker, Spanner, AlloyDB for PostgreSQL, and Cloud SQL without requiring data movement, grounded by Knowledge Catalog semantic models under user-identity IAM governance. Read the announcement blog.
Direct Apache Iceberg access in AlloyDB enables unified lakehouse querying: AlloyDB for PostgreSQL now supports direct querying of Apache Iceberg tables stored in Cloud Storage without requiring ETL pipelines or data duplication. Transactional applications can join operational data with analytical lakehouses in real time using standard PostgreSQL queries, accelerating cross-system analysis while maintaining complete relational integrity. Together with AlloyDB sync and lakehouse federation, you now have three integration approaches to choose from, depending on your use case. Read the launch blog.
AlloyDB Express delivers instant scale-to-zero PostgreSQL for developer prototyping: AlloyDB Express is now available, providing a lightweight, fast-provisioning PostgreSQL environment tailored for developers, testing, and AI coding agents like Antigravity. It scales to zero when idle to minimize costs while maintaining full PostgreSQL compatibility, allowing engineering teams to transition prototype workloads to production AlloyDB instances when ready. AlloyDB Express offers a free tier, with easy-to-understand pricing for higher usage. Read the announcement blog.
Carwow unifies its data foundation with BigQuery, Knowledge Catalog, and Looker to scale AI innovation: Europe's largest online car-changing marketplace migrated to BigQuery, Knowledge Catalog, and Looker to establish a governed single source of truth and enable trusted self-service analytics across the business. Paired with Gemini, Carwow automated vehicle document verification and PII redaction to save £2 million annually while cutting MLOps deployment cycles from months to weeks. Watch the Carwow customer story video.
Geospatial Analytics Playground enables natural-language exploration of BigQuery maps datasets: Google Maps Platform launched the Geospatial Analytics Playground, a natural-language evaluation environment built on top of Places Insights, Roads Management Insights, and Street View Insights datasets in BigQuery. Powered by Gemini, the open-source Agent Development Kit (ADK), and modular agent skills, the playground lets teams test spatial queries and validate dataset coverage without upfront spatial engineering. Read the announcement blog.

November 2 - November 6

BigQuery observability for data agents is now generally available: Data teams can now monitor the performance, adoption, latency, and cost of BigQuery data agents and their multi-turn conversations using Google Cloud Observability. This capability provides production-grade visibility into agent execution traces, usage volume, and query behavior so organizations can scale conversational analytics with confidence. Review the BigQuery release notes.
BigQuery change history and continuous queries for Apache Iceberg managed tables are now GA: BigQuery change history and continuous queries are now generally available for Lakehouse for Apache Iceberg managed tables. You can use the APPENDS table-valued function to track row additions over a specific time range and run continuous queries to read incoming data from and write output rows to Apache Iceberg managed tables. Review the BigQuery release notes.
BigQuery Data Engineering Agent adds support for BigQuery AI and ML functions: The BigQuery Data Engineering Agent now supports BigQuery AI, ML, and object functions and statements directly inside generated Dataform SQLX pipelines and agent instruction files. Paired with new folder-based Git repository integration in BigQuery Studio, data engineers can build, version, and automate AI-enriched data pipelines faster. Review the BigQuery release notes.
AlloyDB for PostgreSQL introduces AI-powered SQL tuning in preview: AlloyDB for PostgreSQL added the ai.tune_sql function in Public Preview. The function combines AI models with query execution plans and runtime statistics to automatically recommend query rewrites, missing indexes, vacuum operations, and database flag adjustments. Review the AlloyDB release notes.
Zero-downtime embedding model upgrades in AlloyDB AI: A new technical guide demonstrates how to upgrade vector embedding models in AlloyDB AI without disrupting production traffic. Using automatic embeddings alongside ScaNN vector indexes, engineering teams can transition to newer embedding models in the background while keeping semantic search and RAG applications online. Read the blog.
Shrink Cloud SQL for SQL Server storage disks with minimal downtime using Database Migration Service: A new technical guide demonstrates how to reclaim underutilized storage and reduce infrastructure costs on Cloud SQL for SQL Server instances using Database Migration Service (DMS). This workflow migrates databases to right-sized target disks with continuous replication so teams can downsize over-provisioned storage with minimal cutover downtime. Read the blog.
Dataflow Streaming Engine adds latency tiers and scheduled autoscaling: Dataflow introduced new horizontal autoscaling controls for Streaming Engine jobs, allowing teams to select low, medium, or high latency tiers to tune backlog sensitivity. Engineers can also configure recurring autoscaling schedules to adjust minimum and maximum worker counts or utilization targets during predictable traffic windows. Review the Dataflow release notes.
Spanner adds ON DELETE SET NULL foreign key actions across GoogleSQL and PostgreSQL: Spanner now supports the ON DELETE SET NULL referential action on foreign keys for both GoogleSQL and PostgreSQL dialects. When a referenced parent row is deleted, Spanner automatically updates the referencing child columns to NULL, simplifying schema migrations and relational integrity management. Review the Spanner release notes.

October 26 - October 30

LookML metadata indexing in Knowledge Catalog is now generally available: LookML models, Explores, Views, and join logic now continuously sync into the Knowledge Catalog context graph. This integration provides turnkey grounding for Gemini Enterprise and custom AI agents with deterministic business math, paired with end-to-end lineage tracing from underlying BigQuery tables directly to individual Looker dashboard tiles. Read the announcement blog.
Best Buy accelerates self-service insights with conversational analytics in BigQuery: Best Buy deployed conversational data agents directly against BigQuery to empower retail teams with multi-turn natural language exploration. The implementation reduces reliance on manual SQL and dedicated engineering queues, accelerating strategic planning and forecasting cycles while preserving enterprise governance and data protections validated against historical sales records. Explore the Best Buy customer story.
Myer accelerates retail insights with BigQuery data agents and Gemini: Leading department store Myer rolled out conversational data agents in BigQuery across 50+ retail analytics projects. Powered by Gemini, the solution enables commercial and merchandising teams to analyze store performance, stock levels, and customer trends using plain English, speeding up decision-making and reducing backlog requests for data teams. Read the Myer case study.
Rightmove speeds real estate analytics and predictive forecasting with Looker: The UK's top property portal unified its business analytics on Looker and BigQuery, delivering 4x faster reporting for 400+ team members and cutting meeting prep from hours to minutes. In addition, Rightmove built forecasting models that predict lead volume within 2–4.5% of actual results, while piloting Conversational Analytics in Looker. Read the Rightmove customer story.
BigQuery adds AI.TRANSLATE and AI.CHUNK_DOCUMENT for in-database text and RAG pipelines: BigQuery introduced two native generative AI capabilities to simplify unstructured data workflows directly within SQL. The AI.TRANSLATE function enables multilingual translation across Neural Machine Translation and Translation LLMs, while the AI.CHUNK_DOCUMENT table-valued function partitions unstructured text into semantically coherent segments optimized for vector embeddings and RAG pipelines. Review the BigQuery release notes.
AlloyDB adds in-database ai.extract and ai.classify functions to accelerate operational AI: AlloyDB for PostgreSQL introduced two new AI functions, ai.extract and ai.classify, in Public Preview, allowing developers to extract structured entities and categorize unstructured text directly inside SQL queries without external inference pipelines. These functions add to existing AlloyDB AI capabilities such as summarizing, generating, and filtering data with AI. Review the AlloyDB release notes.
AlloyDB Omni Web UI is generally available for centralized hybrid database management: The AlloyDB Omni Web UI is now generally available, providing a centralized graphical interface to manage hybrid and on-premises PostgreSQL deployments. Database administrators can monitor multi-cluster topologies, inspect columnar engine metrics, diagnose slow queries with Query Insights, and verify AI model connectivity across their database fleet. Review the AlloyDB Omni release notes.
Cloud Spanner adds approximate aggregate functions for high-speed analytics at scale: Cloud Spanner now supports approximate aggregate functions across both GoogleSQL and PostgreSQL dialects. Powered by HyperLogLog++ sketches, these functions generate fast statistical estimates for distinct count metrics with minimal compute overhead, accelerating analytical queries and reporting dashboards across petabyte-scale operational datasets. Review the Cloud Spanner release notes.
Knowledge Catalog introduces self-learning context enrichment and Knowledge Studio (Preview): Knowledge Catalog's enrichment agent now learns from agent and user interactions and from sources such as playbooks, code repositories, and business documents to find and fix missing or outdated context, which teams can package into reusable context sets. Knowledge Studio, the new studio experience in Knowledge Catalog, lets context engineers and data stewards explore their data estate conversationally, navigate a visual context graph, and review changes before they reach users and agents. Based on internal benchmarks, grounding agents in Knowledge Catalog makes them nearly 4x more accurate, with up to 33% lower latency and using up to 75% fewer tokens. Read the launch blog.
Knowledge Catalog now grounds AI agents in the semantic models you already trust (GA): Knowledge Catalog natively ingests semantic models and metric definitions from Looker LookML, BigQuery Graph, dbt MetricFlow, Snowflake Semantic Views, Databricks Metric Views, and SAP Core Schema Notation, and connects them with lineage, data quality, and access controls in a single context graph. Gemini Enterprise, BigQuery conversational analytics, and custom agents retrieve a metric's trusted formula, verified join path, and backing table in one context call, without moving data or rewriting business logic. Read the launch blog.

October 19 - October 23

BigQuery introduces column aliases in GoogleSQL and cross-region table copy GA
GoogleSQL in BigQuery now supports column aliases in SELECT and WHERE clauses (GA opt-in), avoiding nested subqueries and simplifying migrations from Snowflake and Teradata. Cross-region table copying is also now generally available to streamline regional data replication and disaster recovery. Review the BigQuery release notes.
Conversational analytics in BigQuery expands to Canada and adds CSV/PDF export
Conversational analytics in BigQuery is now available in Montréal (northamerica-northeast1) and Toronto (northamerica-northeast2) to support Canadian data residency requirements. You can also now export agent responses — including text, tables, charts, and citations — directly to CSV or PDF (GA). Learn more about conversational analytics in BigQuery.
AlloyDB Omni 18.4.0 is now GA with Advanced Query Insights and external KMS support
AlloyDB Omni 18.4.0 brings PostgreSQL 18.4 to self-hosted and multi-cloud environments, adding the google_insights_v2 extension for granular query execution and wait-event diagnostics. It also adds Thales CipherTrust Manager support for Transparent Data Encryption (TDE) alongside HashiCorp Vault. Explore the AlloyDB Omni release notes.
Cloud SQL for PostgreSQL adds managed connection pooling and Private Service Connect defaults
Cloud SQL for PostgreSQL now supports enabling built-in managed connection pooling on existing Private Service Connect (PSC) instances to scale high-concurrency workloads. PSC is also now the default private IP option for new Cloud SQL instances in new projects to simplify cross-project networking. Review Cloud SQL updates.
Customer momentum: Unico prevents over $5B in fraud on Google's Agentic Data Cloud
Digital identity platform Unico migrated from Databricks Spark/Scala clusters to BigQuery and Spanner at the core of its data architecture. Unifying real-time transaction streams with petabyte-scale analytics enabled low-latency identity verification that has prevented over $5 billion in fraud. Read the Unico case study.
Developer guide: Getting started with Looker Data Apps
Following the debut of Looker Data Apps in preview, a new walkthrough on the Google Developer Community shares practical patterns for building prompt-driven applications inside Looker. Read the guide on the Google Developer Community.

October 12 - October 16

Looker Data Apps are now in Preview to build and host interactive, agent-generated applications natively inside Looker
Generated using natural language prompts, the Antigravity CLI, or Claude Code, these interactive web applications, natively hosted inside Looker, transform business reporting into operational systems of action. Users can test scenarios, adjust parameters, and trigger workflows in downstream platforms while inheriting LookML semantic modeling, row-level security, and access controls with no runtime LLM token costs. Read the announcement blog.
Build end-to-end data science pipelines with the Data Agent Kit
Following the general availability of the Data Agent Kit, a new end-to-end walkthrough demonstrates how data teams can build, test, and schedule machine learning pipelines directly from their preferred code editors. Explore the walkthrough and codelab.
Manage dashboard agents as code to bring CI/CD and version control to LookML dashboards
Data teams can now declare agent parameters, configurations, and prompt instructions alongside dashboard files in code. This integration enables standard developer workflows for conversational BI, including Git version control, peer review, branching, and automated CI/CD validation before agent modifications reach production end users. Read the technical guide on the Google Developer Community.
Simplify AI search with native AlloyDB hybrid search and Reciprocal Rank Fusion
AlloyDB for PostgreSQL simplifies hybrid search architectures by combining vector similarity search and keyword BM25 retrieval within a unified database engine. Using Reciprocal Rank Fusion (RRF), developers can merge lexical scoring and semantic embeddings into a single ranked result without writing a complex SQL query. This unified approach removes the operational overhead of running and synchronizing external search engines alongside operational databases for retrieval-augmented generation (RAG) and search applications. Explore hybrid search in AlloyDB.

October 5 - October 9

Data Agent Kit is now generally available to bring Google Data Cloud to any coding agent
Data Agent Kit provides a free collection of Model Context Protocol (MCP) tools and Google-authored agent skills that connect more than 15 Google Data Cloud services directly to coding agents across VS Code, Antigravity, Cursor, Claude Code, and Codex. This release expands support for developers and data practitioners to inspect schemas, author queries, and build end-to-end data pipelines in natural language directly from their IDE or CLI. Read the announcement blog.
Spanner Omni is now generally available for deploy-anywhere, multi-model workloads
Spanner Omni brings Spanner's distributed SQL, graph, vector, and key-value capabilities to any environment, including on-premises data centers, other public clouds via Kubernetes or VMs, and local developer laptops. Organizations can build and run multi-model applications with consistent APIs and query engines across hybrid and multi-cloud footprints while seamlessly scaling to managed Spanner on Google Cloud. Read the launch blog and explore how Spanner also removed cumulative mutation limits for DML transactions.
AlloyDB delivers PostgreSQL for agents and native BM25 hybrid search
We announced PostgreSQL for agents in AlloyDB, introducing an agentic database architecture engineered for real-time data access at agent scale with workload isolation. In addition, native BM25 ranking is now in Preview for AlloyDB and Cloud SQL, allowing developers to combine keyword search and vector embeddings directly in PostgreSQL without maintaining an external search engine. For hybrid deployments, the AlloyDB Omni RPM Orchestrator is now generally available, and you can dive deeper in our new Build AI apps and agents with AlloyDB eBook.
Lakehouse runtime catalog adds regional endpoints across 22 regions and GA support for flexible column names
Lakehouse runtime catalog regional endpoints are now live across 22 Google Cloud regions across the Americas, Europe, Asia-Pacific, and the Middle East, supporting both the Apache Iceberg REST catalog endpoint and the Apache Hive catalog endpoint to help customers meet strict data residency and sovereignty requirements. In addition, flexible column names for Apache Iceberg tables are now generally available by default across Iceberg external tables and managed tables, enabling seamless querying of columns containing spaces, hyphens, and Unicode characters. We also announced the Preview of cross-cloud caching and connections for the borderless Lakehouse.
Unlock predictive and agent-ready insights with BigQuery augmented analytics TVFs, TabFM, and identity columns
BigQuery introduced augmented analytics table-valued functions (TVFs): six built-in functions that automate metric diagnostics, trend analysis, and pattern discovery directly where your data lives and integrate as skills for AI agents. We also introduced TabFM in BigQuery to bring tabular foundation model capabilities to predictive analytics, alongside BigQuery identity columns to automatically generate sequential surrogate keys and simplify data warehouse pipelines.
Dataflow adds Pause/Resume and NVIDIA RTX PRO 6000 Blackwell GPU support for large-scale AI workloads
New Dataflow capabilities make it easier to run high-throughput streaming and batch AI pipelines at scale, including Pause/Resume job controls and support for NVIDIA RTX PRO 6000 Blackwell GPUs. And for teams running Apache Spark workloads, learn how to mitigate compute stockouts and maximize availability using flexible VMs.
Unlock up to 3x higher QPS and microsecond latency with Memorystore for Valkey 9.1
Memorystore for Valkey 9.1 is now available, delivering up to 3x higher queries per second (QPS) and microsecond-level caching latencies to accelerate high-throughput applications and real-time AI agent state management. Read the blog.
Customer momentum across Google Data Cloud — Yahoo, PayPal, Surescripts, Airwallex, and Lucius AI
Organizations across industries are scaling their data and AI foundations on Google Data Cloud. Yahoo uses flexible VMs in Managed Service for Apache Spark to reduce provisioning failures by 85%, while PayPal migrated mission-critical analytics workloads to Managed Service for Apache Spark to scale and reduce operational overhead. On the database front, Surescripts migrated 30.5 billion annual healthcare transactions to AlloyDB with 99.998% uptime, Airwallex manages more than 5,000 databases with 99.99% availability on Cloud SQL with a team of four DBAs, and Lucius AI runs a 210,000-tender global platform on AlloyDB and MCP with 47x faster ScaNN vector search.

September 7 - September 10

Pub/Sub SMTs can now AI Inference your Gemini Enterprise Agent Platform models!
Pub/Sub AI Inference SMTs allow you to apply inference on an incoming stream of events using models hosted in Gemini Enterprise Agent Platform. The model’s prediction is appended to your event, making it available for downstream processing in your data warehouse (like BigQuery) or operational database (like BigTable). This feature, now generally available, can dramatically simplify or enhance anomaly detection systems you are operating.
PostgreSQL Source Connector is now generally available in Managed Service for Apache Kafka!
Managed Service for Apache Kafka’s PostgreSQL connector allows customers to capture changes from their PostgreSQL database and ingest them into their Kafka infrastructure with low latency. This source connector is compatible with Cloud SQL for Postgres, AlloyDB, and self-managed PostgreSQL databases. Try this along with our entire portfolio of managed connectors, including MirrorMaker 2.0, BigQuery, Cloud Storage, and Pub/Sub! E-mail kafka-hotline@google.com if you have questions or feedback!

Pause-on-failure for Dataflow batch jobs is GA
Dataflow pause-on-failure enables you to preserve the state of a batch Dataflow job before it fails. By pausing your Dataflow job, you can address issues that are external to the pipeline and resume processing without losing completed work. This helps you better manage resource costs and improve job reliability when you face temporary outages or capacity constraints.

The insertAll API is now the BigQuery Storage Write API (REST)
The legacy insertAll streaming API is now rebranded as the BigQuery Storage Write API (REST). By dropping the "legacy" label, developers can confidently build long-term HTTP-based streaming workflows. This stateless JSON-over-HTTPS endpoint offers a lightweight alternative to heavy gRPC libraries—ideal for serverless web apps, IoT telemetry, and AI logging. The transition is seamless for existing users, requiring zero code changes and offering 100% backward compatibility. However, the Storage Write API (gRPC) version remains the recommended standard for high-throughput, continuous pipelines.

August 31 - September 4

Stateful processing is available in BigQuery continuous queries in Preview
Stateful operations significantly expand what’s possible with BigQuery continuous queries. This feature allows users to leverage functions like JOINs, aggregations, and windowing functions directly in their streaming queries. Now you can calculate metrics over time (for example, a 30-minute average) to power your downstream applications and AI agents with much richer, real-time signals.

Try out our feature here and share your feedback with bq-continuous-queries-feedback@google.com!
Synthetic data generator tool is available for Managed Service for Kafka
You’ve launched your first Kafka cluster. Now what? The next thing to do is to produce some data to the cluster, but that involves modifying a client application somewhere or spinning up a virtual machine. The synthetic data generator tool, now generally available, can start sending mock data to your cluster in 3 clicks, and will get data streaming into your cluster in less than two minutes. The perfect utility for those moments you just want to test your cluster and new features. Try our quickstart today!

Dataflow pipeline updates are faster & more flexible
Dataflow pipeline updates can now stop-and-replace pipelines, a major addition to the existing in-place-update feature. The new parallel pipeline option accelerates the migration between the old & new pipeline, resulting in reduced disruption to your business. You can also set a timeout on drains that prevents runaway costs for your pipeliness in the event of stuck processing. This feature is generally available. Try it here!

July 6 - July 10

New Lakehouse managed tables now in preview
Lakehouse tables for Apache Iceberg are now in preview and available in the console. By using Google-managed Apache Iceberg tables in Lakehouse, you can eliminate the overhead of maintaining duplicate data pipelines and complex synchronization logic between BigQuery and open-source engines. This unified table format delivers native, multi-engine read and write interoperability, allowing you to run concurrent DML/DDL operations across diverse analytics tools on a single, shared storage layer. Built-in automated table management handles painful background optimization tasks like compaction and partition tuning, freeing up your team to focus on building rather than managing storage maintenance.

June 1 - June 5

Beyond the Query: Powering AI Agents with Bigtable, Firestore & Memorystore
Discover the latest advancements in Google Cloud's NoSQL Database portfolio, including Bigtable, Firestore, and Memorystore. This series is designed for a broad audience: whether you are exploring these databases for the first time or are an existing user looking to leverage the new capabilities announced at Next '26.

Register here to secure your spot!

Cloud Engineer's AI Toolkit Workshops: Solve data-driven challenges with BigQuery, AlloyDB, Gemini and more. Hosted by Google Cloud Labs, this highly technical event is built specifically for Platform Engineers, SREs, and cloud infrastructure teams ready to bridge the gap between AI prototypes and production-grade deployments. Look out for more locations coming soon

Toronto - June 25 (Data Cloud) | RSVP Here
Chicago - June 30 (Data Cloud) | RSVP Here
Start a 10-day Bigtable free trial with a 1 node SSD cluster and up to 500GB of storage capacity. With no credit card required to start, you can easily ingest workloads and manage workloads that require low-latency, high-throughput, and predictable access. Plus, new Google Cloud customers get $300 in free credits on signup.

May 11 - May 15

Managed Service for Apache Airflow has launched a wave of new features, including the general availability of Airflow 3.1, AI-powered agentic troubleshooting, a new managed Airflow MCP Server for custom agent integration, and declarative YAML-based orchestration pipelines—discover all the details in the full blog post.

April 20 - April 24

Google-built ODBC Driver for BigQuery is now available in Preview
We are excited to announce the launch of the new, Google-built ODBC driver for BigQuery. This new open-source driver provides a direct, high-performance connection for applications to BigQuery and is developed entirely in-house by Google. Download a new driver and connect your application to BigQuery.

April 13 - April 17

We announced we are reintroducing Data Studio to play a significant role in the AI era, expanding from data visualizations and reports to host BigQuery conversational agents and data apps built in Colab notebooks.
We announced BigQuery Graph is now available in preview, offering an easy-to-use, highly scalable graph analytics solution, empowering data professionals to model, analyze and visualize massive-scale relationships in an entirely new way.

April 6 - April 10

We introduced Conversational Analytics for Looker Embedded environments, enabling users to add natural language experiences to their own custom data-driven applications, powered by Gemini.
We expanded Looker’s capabilities for faster ad-hoc analysis, with the introduction of self-service Explores, enabling you to bring your own data to Looker’s semantic layer and gain instant access to insights in a governed data environment.

March 23 - March 27

We showed you how you can scale your reads with Cloud SQL autoscaling read pools. This feature allows you to provision multiple read replicas that are accessible via a single read endpoint and to dynamically adjust your read capability based on real-time application needs.
Our customers are leveraging the full power of Conversational Analytics and Looker to drive major business and technical breakthroughs in the AI era. Companies like Telenor, Pet Circle, Fluent Commerce, Lighthouse Intelligence, Wego, and ROLLER are turning data into insights and actions, grounded by Looker’s semantic layer.

March 16 - March 20

We introduced an enhanced Gemini assistant in BigQuery Studio, transforming the agent from a code assistant into a fully context-aware analytics partner.

February 23 - February 27

We introduced managed and remote MCP support for Google Cloud databases, including AlloyDB, Spanner, Cloud SQL, Bigtable and Firestore, to power the next generation of agents. This announcement extends the ability for AI models to plan, build, and solve complex problems, connecting to the database tools our customers leverage daily as the backbone of their work environment.
We outlined how you can build a conversational agent in BigQuery using the Conversational Analytics API to help you build context-aware agents that can understand natural language, query your BigQuery data, and deliver answers in text, tables, and visual charts.

February 16 - February 20

Our customers are leveraging the full power of Looker to drive major business and technical breakthroughs. Companies like Arrive, Audika, Carousell, Framebridge, GumGum, Intel, Overdose Digital, Ocean Network Express, Subskribe and Promevo are leveraging Looker’s newest AI-driven capabilities, including Conversational Analytics, to transform data to insights and actions, and empower their entire organization with a single source of truth, powered by Looker’s semantic layer.

February 2 - February 6

Join us on March 4 for our webinar, Win Your AI Strategy with Cloud SQL Enterprise Plus, to learn how to power your generative AI workloads with 3x higher performance and 99.99% availability. Register today to discover how to build a scalable, enterprise-grade foundation for your most demanding AI applications.

January 26 - January 30

We introduced Conversational Analytics in BigQuery, which allows users to analyze data using natural language. Conversational Analytics in BigQuery is an intelligent agent that generates, executes and visualizes answers grounded in your business context directly in BigQuery Studio, making data insights for data professionals more conversational.
We outlined how data products have become the foundation for AI agents, providing the context needed to make autonomous agents reliable and trusted for real business use, backed by organized business logic and semantic understanding.
We highlighted how you can supercharge data analytics workflows, and outlined Google Cloud’s AI agent offerings for data engineering, data science, and development tools, so you can integrate agentic workflows in your applications, empower your teams and speed discovery.

January 19 - January 23

We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.
Introducing Google Cloud SQL on MSSQLTips: We are highlighting a new technical guide published on MSSQLTips titled "Introducing Google Cloud SQL." This article serves as an essential resource for SQL Server administrators and developers exploring Google Cloud's fully managed database service. It provides a detailed overview of Cloud SQL capabilities, including high availability, security integration, and the seamless transition of on-premises SQL Server workloads to the cloud, making it an ideal resource for those planning their migration strategy.
We are excited to announce the Public Preview of Microsoft Entra ID (formerly Azure Active Directory) integration with Cloud SQL for SQL Server. Designed to tackle the challenge of identity sprawl in multi-cloud environments, this integration allows organizations to govern database access using their existing Microsoft identity infrastructure. Key benefits include centralized identity management, enhanced security features like Multi-Factor Authentication (MFA), and simplified user administration through direct group mapping. This feature is available for SQL Server 2022 and supports both public and private IP configurations.

January 12 - January 16

Google-built JDBC Driver for BigQuery is now available in Preview
We are excited to announce the launch of the new, Google-built JDBC driver for BigQuery. This new open-source driver provides a direct, high-performance connection for Java applications to BigQuery and is developed entirely in-house by Google. Download a new driver and connect your Java application to BigQuery.
Troubleshoot Airflow tasks instantly with Gemini Cloud Assist investigations: Cloud Composer just got smarter. We are excited to announce that Gemini Cloud Assist investigations are now available directly within Cloud Composer 3. Instead of manually sifting through raw logs, you can now simply click "Investigate" on a failed Airflow task. Gemini analyzes logs and task metadata to identify failure patterns—such as resource exhaustion or timeouts—and provides actionable recommendations driven by Gemini Cloud Assist to resolve the issue. This integration shifts the debugging experience from manual toil to automated root cause analysis, significantly reducing the time required to restore your pipelines. Learn more about AI-assisted troubleshooting.

Autonomous defense at cloud speed with SentinelOne and Google Security Operations

Thu, 12 Nov 2026 17:00:00 +0000

Modern security operations centers (SOCs) face an ongoing pipeline dilemma. While ingesting comprehensive endpoint telemetry is non-negotiable for defense, conventional delivery paths often drive up transfer overhead and alert latency. As attackers operate at machine speed, any unnecessary friction in how data moves from endpoints to the SOC can leave teams without the insight they need to respond to attacks in real time.

Addressing this requires optimizing the data pipeline at the integration layer. Rather than routing telemetry through complex intermediary cloud buckets that require ongoing maintenance and introduce costly processing delays, engineering teams need a streamlined delivery architecture. By eliminating architectural bottlenecks and optimizing data transit, security operations and IT leaders can significantly compress ingest latency, lower operational overhead, and give analysts near-instant visibility where it matters most.

Google Cloud and SentinelOne collaborated to solve this scaling challenge with a fundamentally re-architected integration. As a Google Unified Security Recommended endpoint detection and response (EDR) partner, SentinelOne pairs with Google Security Operations through direct integration and streamlined data schemas, delivering essential endpoint telemetry visibility to Google Security Operations detection and agentic SOC pipelines while significantly reducing the total cost of ownership of the integration.

What’s at stake?

For enterprise customers, every minute of telemetry latency can increase the blast radius of an active intrusion. When detection pipelines take half an hour to surface adversary activity, incident response transitions from active mitigation to retrospective damage control.

For the broader security community and IT leadership, inefficient ingestion pipelines can impact budget and compute resources. Retaining bulky, unoptimized telemetry in intermediate staging environments diverts resources away from strategic security initiatives and saddles engineering teams with unneeded infrastructure debt.

Technical highlights and capabilities

The re-engineered integration between Google Cloud and SentinelOne introduces six dedicated engineering workstreams designed to remove ingestion bottlenecks and reduce data overhead without sacrificing detection fidelity:

Significant event size reduction: A modernized EDR data schema greatly reduces the average event size, preserving full analytical fidelity and reducing downstream compute load for agentic workflows.
Direct Google API ingestion: SentinelOne directly streams events into Google Security Operations with APIs, eliminating intermediate cloud storage hops and associated storage fees.
Reduced streaming latency: Removing the requirement for intermediate cloud storage makes data available for detection, investigative, and agentic workflows in Google Security Operations in near real-time.
Precision telemetry scoping: Integrated SentinelOne Cloud Funnel filters allow further flexibility to customize telemetry sent to Google Security Operations.
Unified alerts API and Mandiant alignment: Parsers, alert models, and SOAR playbooks were engineered alongside Mandiant Incident Response and Managed Defense teams to support automated host isolation, full-disk deep visibility scans, malware extraction, and artifact enrichment directly from Google Security Operations.
Industry-validated partnership: Named Google Threat Intelligence's Intel Partner of the Year at Google Cloud Next.

Transform your SOC ingestion today

Modern defense requires direct action, not delayed transit. By cutting out unnecessary staging layers and streaming directly via APIs, your security team gains the speed needed to outpace modern threats while protecting your budget.

Targeted for phased delivery by end of year, initial rollouts are prioritizing customers using Mandiant Managed Defense and Mandiant Threat Defense. Contact your Google Cloud or SentinelOne account team today to evaluate your current telemetry pipeline, schedule a technical architecture review, and reserve early access for your environment.

Google named the furthest in vision for the Gartner Magic Quadrant for Data Lakehouse Platforms

Thu, 12 Nov 2026 16:00:00 +0000

Gartner has named Google a Leader, positioned furthest in vision, in its first-ever Magic Quadrant for Data Lakehouse Platforms. In our opinion, this recognition validates a fundamental shift our customers are experiencing every day: In the agentic era, the enterprise lakehouse is no longer a passive data repository, but a foundation where agents reason and act across all your data.

The lakehouse originally emerged to bring data warehouse reliability to open, low-cost storage, but the benchmark defining the category is now shifting. Leading enterprises are moving rapidly from static reporting to autonomous AI agents that monitor operations, flag anomalies, and execute business workflows in real time. To support that shift, organizations need a platform where thousands of agents can read, reason, and act continuously across enterprise data without having to move that data first. Traditional, fragmented data stacks burdened by brittle pipelines, duplicated storage, and disjointed governance simply were not designed for the scale, performance, and latency required for agentic-scale workloads.

We engineered our borderless Lakehouse at every layer of the stack to eliminate this complexity and hidden cost. Specifically designed for agents to reason across petabyte-scale data, the borderless Lakehouse unifies your data estate across clouds, data platforms, and SaaS applications so you can ground agents in governed business context and bring Gemini directly to your data. This allows your teams to move from managing infrastructure to orchestrating business outcomes.

Activate your global data footprint in place

For most organizations, the biggest barrier to scaling AI is that enterprise data lives everywhere, spanning multiple clouds, operational databases, and SaaS platforms. Moving all of that data into a single physical location is slow, expensive, and introduces governance risk.

Our borderless Lakehouse acts as the bridge across your distributed environment to activate your data. Built on open Apache Iceberg, it connects your multi-cloud, SaaS, and operational data into a single governed fabric. Organizations can start analyzing datasets across AWS, Azure, and Google Cloud immediately without costly data copies or complex migrations, benefitting from:

Openness without performance trade-offs: Run analytics and AI across BigQuery and Managed Service for Apache Spark directly on open Iceberg tables with similar price-performance as native storage.
High-speed cross-cloud economics: Establishing private, high-speed paths to AWS and Azure is simple and cost-effective through Cross-Cloud Interconnect and intelligent caching. By caching only the active data blocks your queries touch, the lakehouse serves repeated lookups locally, protecting data residency and eliminating variable egress fees.
Vertically integrated security and governance: Keeping data protected wherever it resides is non-negotiable. Credential vending in Lakehouse runtime catalog helps ensure security policies travel directly with your agents wherever your data resides.

German banking leader Commerzbank built its AI strategy on our borderless Lakehouse and Apache Iceberg to deploy AI agents across contact centers and marketing while satisfying strict European banking regulations. A unified foundation now powers AI across the bank. The marketing team reduced campaign production times from weeks to days and empowered contact center agents with verified next steps in seconds.

Agents need business context, not just access

An autonomous agent is only as accurate as the business context it understands. The lakehouse bridges the gap between agents and enterprise data, automatically harvesting metadata directly from your storage layers and passing it to your context engines. The Lakehouse runtime catalog integrates directly with Knowledge Catalog, which acts as your universal context engine for your enterprise data. It autonomously aggregates and curates metadata and semantic context from all your sources, within and outside Google, enriches them to extract meaning, and serves this unified context to all the agents that you build and use. Key Knowledge Catalog capabilities include:

Unified aggregation across the enterprise: A single, governed semantic layer prevents metric drift across multi-cloud assets. With the general availability of expanded aggregation in Knowledge Catalog, organizations can unify metadata, metrics, and business logic across Looker, dbt, and BigQuery in one plane.
Turnkey agent grounding: Anchoring agents in verified business logic accelerates development and builds trust. Gemini grounded with Knowledge Catalog and context sets, both in preview, let teams package curated metrics and instructions into token-efficient bundles that agents consume via standard APIs and Model Context Protocol.
No-code context optimization: Delivering only the most relevant context reduces latency, lowers token costs, and prevents expensive reasoning loops. With a new studio experience in Knowledge Catalog, in preview, data stewards get an agentic-first, no-code environment to resolve definition gaps and securely govern the exact context that agents are authorized to access.

Bloomberg Media grounded their conversational queries directly in the Knowledge Catalog semantic layer, achieving a 63% improvement in query accuracy and eliminating hallucinations across their business teams.

Business context is only half the equation. Because agents act rather than just analyze, your analytical lakehouse and operational databases have to behave as one system. That is why BigQuery, Managed Service for Apache Spark, Spanner, and AlloyDB are built to work together, making the borderless Lakehouse the data foundation for the entire Agentic Data Cloud.

Empowering practitioners with an agentic lakehouse

Unlocking the full potential of your lakehouse requires transforming how people work with data. Historically, data engineers, data scientists, and business users operated in disconnected silos. The lakehouse acts as the collaborative connective tissue by integrating directly with Gemini, allowing you to build, test, and operate AI agents directly on top of your unified lakehouse data.

Intent-driven workflows for technical teams: With the Data Agent Kit, data practitioners can write code, build notebooks, and run ML models directly from their preferred IDEs and CLIs using simple text prompts. Instead of writing boilerplate code from scratch, teams can use pre-built agentic skills and MCP tools to build complex data pipelines and work seamlessly across 15+ Google Cloud data services
Proactive and personalized insights for business users: Conversational analytics gives business users trusted answers in plain language, without waiting on data teams to build custom reports. One-click publishing extends this, instantly turning approved technical models into conversational tools, allowing employees to ask plain-language questions, run accurate reports, and safely ground custom agents in daily workflows without any coding experience.

PayPal connected their Looker semantic models directly to AI agents using these capabilities, allowing business intelligence teams to define metric models once so that 5,000 employees could query live data with near 100% accuracy, accelerating insights by 50%.

Bringing AI-powered engines to the Lakehouse

To make decisions that impact your business in real time, AI agents need to run complex reasoning loops directly where your data lives. We bring Google's leading artificial intelligence and high-performance processing engines directly into open table formats, enabling you to extract massive value without the complexity of duplicating data. Consider the following use cases:

Running analytics at unprecedented scale: Driving real-time actions over petabytes of open data requires highly optimized compute. In BigQuery we've made queries over 40% faster while cutting query processing cost by more than 50% through intelligent optimizations like fluid scaling and built-in workload acceleration, while our Lightning Engine for Apache Spark runs open-format Spark workloads with an over 3x price-performance advantage over the leading alternative.
In-place AI and machine learning: With TabFM in preview, business teams can generate instant predictive insights on tabular data through simple SQL queries, while TimesFM 3.0 in BigQuery provides advanced multivariate forecasting over existing data.
Cost-effective agent-scale data processing: Agents query data continuously and in short bursts, so paying for idle compute quickly adds up. BigQuery fluid scaling introduces per-second billing with no minimum duration, removing the costly one-minute minimum to deliver up to 34% in compute cost savings.

Maximize efficiency at scale without cost spirals

Google Cloud's borderless Lakehouse redefines enterprise AI economics by delivering compounding savings across compute, AI processing, and token consumption. As agents apply AI across millions of rows, token spend can quickly become a very large line item. Knowledge Catalog prevents expensive token consumption by filtering and delivering only the precise metadata and metrics required for each prompt, avoiding the need to send massive, unorganized datasets to your LLMs, significantly reducing token consumption. BigQuery AI adds built-in token controls that let teams estimate usage before a query runs, set firm limits, and automatically switch to smaller, more efficient models through an optimized mode. The result is BigQuery's cost-optimized AI functions use 230x fewer tokens compared to row by row gen AI processing.

What’s next?

To learn more about our placement and how we think we can accelerate your data journey, download the complimentary 2026 Gartner Magic Quadrant for Data Lakehouse Platforms report.

_{Gartner, Magic Quadrant for Data Lakehouse Platforms, Aaron Rosenbaum, Michael Gonzales, Masud Miraz, Ramke Ramakrishnan, Xingyu Gu, Robin Schumacher, Prasad Pore November 4, 2026}_{Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.}_{Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.}_{This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google.}

Introducing new AI functions for AlloyDB: ai.classify and ai.extract

Wed, 11 Nov 2026 17:00:00 +0000

Transactional databases store vast amounts of unstructured text — clinical notes, customer reviews, support tickets, invoices, and property descriptions. Historically, working with this text meant building complex application code: You had to export data from the database, call external model endpoints, write custom prompt wrappers, parse unpredictable responses, manage retry logic, and update database records. This application-side loop creates operational friction. It introduces query latency, bloats application code, increases model token costs, and creates data compliance risks by moving sensitive information across multiple network boundaries.

We built AlloyDB AI to bring Gemini world knowledge directly into your operational database. Rather than treating artificial intelligence as a disconnected external service, we integrated model intelligence directly into the database engine. Today, we are announcing the preview of two new SQL functions in the AlloyDB AI query engine — ai.classify and ai.extract — available in the google_ml_integration extension (version 1.6.1 or later) on PostgreSQL 18, allowing you to perform classification and structured extraction directly inside your database using standard SQL. Supported by smart batching for AI functions (previously known as AI Function Acceleration), these functions deliver production scalability and performance.

Now, let’s take a look at these new functions and how to use them.

Classifying text directly in SQL with ai.classify

The ai.classify function categorizes text into user-defined classes without requiring manual prompt engineering. You can specify categories as a simple list of labels or provide a list containing label names and descriptions to guide model context.

Key capabilities include:

Single-label classification (output_mode => 'single') to assign the single best category
Multi-label classification (output_mode => 'multi') to return all matching categories
Category descriptions using 2D arrays to provide explicit context for each label
Few-shot prompting by supplying inline examples
Flexible execution modes for individual rows (TEXT), arrays (TEXT[]), and cursors (REFCURSOR)

Here’s a quick example showing how you can classify customer feedback directly within a SQL query:

code_block: <ListValue: [StructValue([('code', "SET google_ml_integration.enable_preview_ai_functions = TRUE;\r\n\r\nCREATE TABLE product_reviews (id INT, review TEXT);\r\n\r\nINSERT INTO product_reviews VALUES\r\n(1, 'Fantastic, would buy again!'),\r\n(2, 'Broken on arrival.'),\r\n(3, 'Mediocre, not worth the price.');\r\n\r\nSELECT unnest(\r\n ai.classify(\r\n inputs => ARRAY(SELECT review FROM product_reviews ORDER BY id),\r\n categories => ARRAY[\r\n ['positive', 'Good sentiment'],\r\n ['neutral', 'Neither good nor bad sentiment'],\r\n ['negative', 'Bad sentiment']\r\n ],\r\n batch_size => 5\r\n )\r\n) AS sentiment;\r\n\r\n-- Expected Output:\r\n-- sentiment\r\n-- ---------\r\n-- positive\r\n-- negative\r\n-- negative"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea9611ed0>)])]>

Extracting structured JSON with ai.extract

The ai.extract function transforms unstructured text into structured JSON data matching your target schema — specified as a JSON schema or a text list of fields. It handles system instructions internally, saving you from writing prompt templates or response parsers.

Key capabilities include:

Schema flexibility supporting standard JSON schemas or plain text field lists
Automatic transformation of unstructured text into machine-readable JSON
Overloaded function signatures for single rows, arrays, and cursor batching

Here’s an example extracting clinical fields from free-form medical notes:

code_block: <ListValue: [StructValue([('code', 'SET google_ml_integration.enable_preview_ai_functions = TRUE;\r\n\r\nSELECT ai.extract( \r\n input => \'Patient Jane Smith, 52 years old, visited the clinic on Oct 25. She is currently experiencing a mild fever, persistent cough, and severe body aches. The doctor prescribed Ibuprofen and Amoxicillin.\',\r\n output_schema => \'{\r\n "patient_name": {"type": "string"},\r\n "age": {"type": "integer"},\r\n "symptoms": {\r\n "type": "array",\r\n "items": {"type": "string"}\r\n },\r\n "prescribed_medications": {\r\n "type": "array",\r\n "items": {"type": "string"}\r\n }\r\n }\'\r\n);\r\n\r\n--- Expected output \r\n-- {\r\n-- "patient_name": "Jane Smith",\r\n-- "age": 52,\r\n-- "symptoms": [\r\n-- "mild fever",\r\n-- "persistent cough",\r\n-- "severe body aches"\r\n-- ],\r\n-- "prescribed_medications": [\r\n-- "Ibuprofen",\r\n-- "Amoxicillin"\r\n-- ]\r\n-- }'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea8a0e410>)])]>

Speeding up queries with smart batching

Running AI evaluations row-by-row can slow down query execution. AlloyDB addresses this with smart batching for AI functions. When you use ai.classify or ai.extract in SQL WHERE clause expressions, the database engine automatically groups inputs into optimized micro-batches before sending them to Gemini Enterprise Agent Platform models.

Smart batching delivers significant performance and efficiency improvements:

Faster inference for query execution compared to row-at-a-time evaluations
Reduces total model token spend by optimizing input prompt overhead

Real-world industry applications

Native classification and extraction functions support operational workflows across key industry sectors:

Healthcare: Securely convert free-text clinical notes into structured records containing patient names, symptoms, and prescribed medications, keeping patient data safely within the confines of the database.
Financial services: Extract vendor details, invoice dates, and line-item totals from scanned document text.
Legal technology: Pull governing law, liability caps, and termination notice periods from contract documents.
Real estate: Extract property types, bedroom counts, and price ranges from descriptive listing text.
Knowledge graphs: Extract entities and relationships from documents to populate PostgreSQL Property Graph (PGQ) structures.

Next steps and getting started

We are continuing to expand in-database AI capabilities in AlloyDB, with upcoming enhancements planned for advanced cost optimization, line-level provenance tracking, and query pushdowns beyond WHERE clauses.

To start using ai.classify and ai.extract in your database:

Review the AlloyDB AI Query Engine documentation.
Explore the AlloyDB release notes.

Don’t just prompt AI — architect it: Introducing the Professional Agentic Architect Certification

Wed, 11 Nov 2026 17:00:00 +0000

It just got easier to prove your expertise in building production-ready agents. Today, Google Cloud is introducing the Professional Agentic Architect certification as the latest milestone in our Gemini Agent Ready (GEAR) initiative. Nearly one million developers are using GEAR to skill up on agents, but what makes this particular certification so special is its adoption within Google’s walls.

Over the past four months, Google Cloud has trained thousands of forward deployed engineers and strategic partners on our own advanced curriculum. Originally built as an internal training initiative, we are now opening it up to the entire developer community and partner ecosystem through this new certification.

As the VP who manages our forward-deployed engineers here at Google Cloud (Ravi) and the managing director of Google Cloud learning (Gary), we’ve seen just how effective this program has been inside Google at helping our teams rethink the way they build, deploy, and use AI in everyday in almost every way. We’re equally about what this new certification can bring to everyone — developers, engineers, and anyone else ready to demonstrate their agentic leadership.

A new hybrid format to validate your real-world skills

This is not a traditional multiple choice exam. Google has introduced a new hybrid format that requires you to prove both conceptual know-how and hands-on execution.

Part 1 (The multiple choice proctored exam): Evaluates your system design choices, architectural standards, cost optimization, and security governance.
Part 2 (Hands-on labs on Google Skills): Puts you in an active cloud environment to build, test, and debug live agentic workflows.

Why earning this certification can help advance your career

As enterprises look to find top AI talent, this certification immediately signals that you have the advanced skills necessary to design secure, governed, and high-performance multi-agent systems.

Our research with Ipsos on AI skill building shows that certifications drive more recruiter outreach: 82% of learners using Google Skills say that mentioning Google Cloud certifications on their resume drives more recruiter responses.

And it’s not just about getting the call — it’s about being able to answer it. That same research showed that 90% of cloud professionals say credentials like Google Cloud certifications give them the verified know-how needed to fill in-demand roles.

What’s covered on the exam

The certification validates your proficiency across five key domains:

Build agents using low-code tools (democratizing rapid agent prototyping).
Use coding agents for application development (supercharging your coding productivity).
Develop custom agents on the Gemini platform with open source frameworks.
Evaluate and deploy agentic workflows (monitoring, optimizing, and ensuring performance).
Secure and govern agentic workflows (protecting sensitive enterprise data and ensuring model safety).

Battle-tested by thousands of Google Cloud engineers and partners — now available to you

Enterprise demand for agentic workloads has skyrocketed. As our customers move past AI prompting and run their most business critical workloads on Google Cloud, we’ve built our own team that delivers the agentic future with them — at scale. Google Cloud partners and Google’s forward deployed engineers are embedded directly with customers around the world and are prepared to solve deep technical challenges because of this rigorous curriculum and certification.

While building our forward-deployed engineering program, we wanted to standardize training and upskilling for customers, Google Cloud partners, and our own engineers with the Professional Agentic Architect certification. This ensures our own teams are using the same world-class standards that we recommend to our customers and partners, allowing us to learn from each other and sharpen our skills together.

Skill up for your certification at no cost today

Register for Gemini Agent Ready to get access to 35 free monthly learning credits on Google Skills. For Google Cloud customers, Get Certified provides a cohort-based, guided certification journey at no cost.
For Google Cloud partners, register for Partner Certification Kickstart and review the partner learning path.
Review the exam guide, learning path and sample questions.
Get hands-on with Google Skills labs and preparation modules to get comfortable developing custom agents on Gemini platform with open source frameworks.
Register for the multiple-choice exam, complete the hands-on lab requirements, and claim your digital badge to showcase your expertise to the world.

Don't just prompt AI — architect it. Get certified today.

How lemonflow built real-time agents for EV charging using Gemma and TPUs

Wed, 11 Nov 2026 17:00:00 +0000

Welcome back to The Blueprint, a regular feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to inspire others looking to innovate in their work.

As the electric vehicle revolution gains steam in Europe, every charging session is a moment of truth, for the driver and for the transition itself. At lemonflow, we build AI agents purpose-built for EV charging: support agents for drivers and monitoring agents for charging networks. Our support agents answer drivers around the clock in more than 90 languages, and today they help an EV driver every 90 seconds.

The Challenge

Before we founded lemonflow, we worked closely with leading mobility players across Europe and saw first-hand how often charging goes wrong: Between 15% and 25% of EV charging attempts in Europe end in failure. Across Europe’s rapidly expanding EV market, where battery-electric car sales grew 30% in 2025 and thousands of charge point operators run public chargers, drivers who encountered stalled charging points were often left stranded or stuck in slow call center queues.

We set out to change that hands-on: to bring the cutting edge of AI to EV charging, and to build the world’s best conversational AI for distributed hardware, an automated platform that resolves real-time charging issues via phone, chat, email, and WhatsApp. However, handling live voice support created several technical requirements:

Strict latency thresholds: Frustrated drivers want immediate assistance. Our testing confirmed that serving latencies above 800 milliseconds risk conversational barge-ins, causing drivers to interrupt the agent and break the interaction flow.
Managed API bottlenecks: Initially relying on managed LLM APIs, we faced latency deviations during peak hours (spiking from 1.0–1.2 seconds to over 1.7 seconds) and experienced HTTP 429 rate-limit errors when global usage peaked.
Compute-intensive conversations: Every agent works from one comprehensive prompt, paired with dynamic tool calls that read and act on live charger data. This design creates intense bursts of compute demand during calls, making throughput on shared, managed capacity difficult to guarantee under peak loads.
Highly fragmented ecosystem: We needed a standardized conversational engine capable of bridging around 30 distinct Charge Point Management Systems (CPMS), the backends operators use to run their chargers, each with its own APIs, data models and quirks.
Hundreds of tailored agents: No two operators run support the same way. Each has its own tariffs, payment methods, escalation paths and hours for human backup, and most run several agents, for example one per country, brand or hotline, with behavior that can change by time of day. Each agent’s prompt is unique and must be served at full speed and improved without breaking any of the others.

To deliver ultra-low-latency voice interactions and guaranteed serving capacity, we needed direct control over our model hosting environment.

The Solution

We wanted every driver to get best-in-class support that feels effortless, almost magical: no queue, no menu, just a short conversation that ends with the car charging. Picture a driver on a long cross-border trip, pulling into a motorway service station at 11 p.m. She plugs in, but the charger won’t start: the previous session never closed properly. She calls the number on the charger and is answered in her own language. Within seconds, our agent spots the stuck session in the charger’s logs, restarts the charger remotely and stays on the line until power is flowing.

To get there, we had to solve our latency bottlenecks and secure dedicated compute capacity. We migrated from managed APIs to self-hosting the open Gemma 4 model on Google Cloud TPU v6e hardware in the EU.

Like many startups using Gemma, the compatibility of these open models with Gemini allowed us to transition our existing prompt infrastructure with minimal modification.

Moving to self-hosted TPUs unlocked the more complete architectural control our engineering team required to establish a deterministic core layer that handles EV-specific guardrails, guards against prompt injections, and standardizes previously fragmented backend protocols into our internal format before ever reaching the LLM. Control had a second payoff: because the model and serving stack change only when we change them, every test result stays meaningful. That stability laid the foundation for Agent Coach, our background coaching engine for AI agents, described below.

We then developed an intelligent routing gateway to prioritize workloads according to operational urgency, serving live voice calls first, followed by web chats, and deferring automated re-simulations to low-traffic periods.

Custom optimizations to the open-source vLLM serving engine and memory tiering between the TPU and host memory let a single TPU slice keep many more operator agents ready for instant answers, without recalculating their context.

We also implemented automated fallback routing to self-hosted GPUs and Gemini Enterprise endpoints. Thanks to TPU reliability, Gemma answers more than 99.9% of all live conversation turns, and these fallbacks serve primarily as deployment buffers during system updates. Primary or fallback, every request is served from secure EU compute.

The Architecture

The Outcome

For the e-mobility companies we work with, and for their drivers, the result is support that simply works: our agents typically answer every call, handle more than 80% of conversations end to end without a human handover, and earn 95% driver satisfaction. Behind the scenes, self-hosting Gemma on Google Cloud TPUs transformed our performance:

Rapid deployment cycles: Prompt update and test cycle times dropped from 2–3 hours to about 10 minutes, a more than tenfold faster improvement loop.
Sub-second serving latency: We lowered our P50 latency to around 350 milliseconds and our P90 latency to around 600 milliseconds. That is roughly three times faster than our managed-API baseline and fast enough to sharply reduce customer barge-ins.
Zero throttling and flat latency: Dedicated TPU infrastructure eliminated HTTP 429 rate-limit errors, and latency stays flat through peak afternoon call traffic.
Testing without limits: On dedicated capacity, every TPU cycle that live conversations don’t need becomes testing capacity, so our team can experiment and validate at will.
Built for scale: Today, our agents support more than 40 charging networks in 17 countries, covering more than 250,000 charge points, around the clock. And this is just the beginning: the same platform is ready to take on many more networks and markets.

With additional compute capacity, we introduced Agent Coach, a background engine that provides agents with relevant context during live calls and turns real conversations into permanent test cases, so every call makes the next one better. We also added nightly automated regression testing across thousands of fixed conversational scenarios.

Looking ahead, we are preparing to move to Ironwood, Google’s seventh-generation TPU, as soon as it is available in our EU region. With six times the high-bandwidth memory per chip, Ironwood will multiply the number of operator agents a single TPU can keep ready in memory for instant answers.

Ultimately, our goal is that no driver is ever left stranded at a charger. They get an instant, expert answer in their own language, the charger is rebooted remotely if necessary, and our AI monitoring agents catch the next fault before another driver ever notices. Open models on dedicated Cloud TPUs give us the speed and control to make that the standard across Europe.

The Details

Our platform runs two kinds of AI agents: support agents that talk to drivers in real time, served by Gemma on Cloud TPUs for minimal latency, and monitoring agents that analyze conversations and charger logs in the background on Gemini, so operators can fix faults before the next driver is affected. To keep its core platform consistent across many kinds of e-mobility companies, from charge point operators (CPOs) and e-mobility service providers (eMSPs) to charger manufacturers, lemonflow built a translation layer that converts varied CPMS interfaces into a standardized format.

When a driver calls the support line posted on a charger, the call reaches our Conversational Engine through the operator’s own hotline. The engine then pulls the charger’s live status and Open Charge Point Protocol (OCPP) logs, the messages chargers exchange with their backend, from the operator’s CPMS in real time. Because charging stations often lack digital displays, drivers are frequently unaware of underlying issues like pending mobile 3D-Secure payment authorizations, session timeouts, charging cards not enabled for roaming, power reduced by local load management, or a car that ended the session at its own charge limit. Our engine feeds structured log summaries directly into Gemma on TPU to inform interactions with drivers.

Direct model control allows us to stream token output selectively. Partial text tokens stream instantly to our Text-to-Speech (TTS) engine, while structured tool-call tokens stream asynchronously to backend systems to execute real-world actions, such as remotely restarting chargers, unlocking stuck cables, stopping sessions that failed to end, sending drivers details via SMS or email, or notifying service technicians.

Deterministic security controls screen inputs before they reach the TPU-hosted LLM, blocking malicious prompts. This architecture is designed with European critical-infrastructure requirements in mind, and it still keeps conversations snappy, with a typical response starting in around 350 milliseconds.

In the end, all of this should stay invisible to the driver: they just get a fast answer and a charger that works. We’re proud to work with Google Cloud to make that the norm and help EV adoption keep growing.

Inside the Gemini factory: Accelerating partner solutions

Tue, 10 Nov 2026 17:00:00 +0000

With the new unified Gemini experience, enterprises can bring third-party partner capabilities directly in Gemini through standardized connectors (spanning Model Context Protocol (MCP), Agent-to-Agent (A2A) runtimes, and REST APIs), alongside emerging declarative skills and plugins.

For independent software vendors (ISVs), this unified architecture unlocks a powerful opportunity to embed domain-specialized intelligence, regulated workflows, and live systems of record directly into enterprises' daily workflows. Yet turning a compelling use case into an enterprise-ready integration often stalls in engineering friction, from choosing the right extensibility primitive and wiring authentication to orchestrating multi-step tool execution and rendering inline interactive experiences.

To eliminate these bottlenecks, Google Cloud launched the Gemini factory—an intensive co-innovation initiative designed to help enterprise software partners go from a blank whiteboard to agentic solutions listed on Google Cloud Marketplace in record time. Through hands-on workshops in global technology hubs—including San Francisco, New York City, Sunnyvale, London, Paris, Tel Aviv, and Tokyo—Google Cloud specialists work side-by-side with partner teams to ideate, architect, and build the next generation of enterprise AI solutions with Gemini.

Specifically, the Gemini factory helps partners:

Choose the right agentic architecture across MCP connectors, skills, plugins, and autonomous agents.
Go from an initial use case to a production integration in days rather than months.
Accelerate customer activation with funding incentives and go-to-market (GTM) support.

Let’s dive in and see how we make all of that a reality.

Choosing the right architecture: agents, MCPs, skills, and plugins

In previous iterations of enterprise AI, third-party integrations often operated as isolated top-level agents, forcing employees to leave their workflow. When software providers arrive at the Gemini factory, they evaluate how their capabilities plug into Gemini across four complementary architectural primitives rather than competing extremes:

Model Context Protocol (MCP) connectors: The foundational data and tool pipe for exposing external APIs, search, and CRUD operations to Gemini as callable tools registered in Agent Registry in Gemini platform.
Declarative Skills (.md): Open Markdown configurations stored in Skills Registry that equip Gemini with domain-specific instructions, operational guardrails, and repeatable multi-step workflows across connectors.
Plugins: The composite packaging and governance primitive in Gemini and Google Cloud Marketplace that bundles one or more Connectors and Skills into a single turnkey solution for IT administrators to govern and Gemini users to invoke.
AI agents (A2A): For enterprise ISVs whose value lies in proprietary algorithms, regulated workflows, or strict compliance rules, standalone A2A agents run in an isolated partner environment while plugging into Gemini as callable connectors—keeping intellectual property protected, guaranteeing deterministic execution sequences, and maintaining tamper-proof audit trails.

Rather than forcing developers into a single mold, Gemini unifies all four primitives into a coherent enterprise fabric—passing shared session context into partner connectors and chaining outputs across the enterprise ecosystem.

Technical velocity: From use case to production

Once the right architectural pattern is defined, the Gemini factory compresses months of integration work into a streamlined, AI-assisted engineering pipeline that brings partner development teams and Google Cloud experts together side-by-side.

Accelerated Partner Development Pipeline

This execution velocity is powered by three core capabilities in Gemini platform:

Antigravity & AI-assisted builder workflows: Rather than writing boilerplate from scratch, developers build and govern solutions in Antigravity, an AI-first development environment. Using pre-built Rapid Agent Development (RAD) skills and AI-assisted workflows, partner teams rapidly translate use-case specifications into working architectures, scaffold remote MCP connectors or agent runtimes, author declarative .md skills, and run automated pre-ingestion validation harnesses.
Flexible backend primitives (MCP, skills, plugins, and ADK): For most integrations, teams expose platform APIs via a hosting-agnostic Remote MCP Connector (on Cloud Run or their existing cloud) and layer on declarative Skills (.md) bundled into a turnkey Plugin. When a use case requires closed-IP models or strict deterministic execution, partners use the Google Agent Development Kit (ADK) and the A2A protocol to expose their runtime as a callable connector in Agent Registry.
Inline adaptive experiences (native MCP apps): Instead of redirecting users to fragmented external portals or maintaining complex custom UIs, Gemini keeps users in the flow of work. Through native MCP apps and guided inline components, partner integrations dynamically stream rich, context-aware visual canvases directly in Gemini while chaining outputs across Google Workspace and other enterprise connectors.

From working prototype to marketplace distribution

The Gemini factory is structured as an end-to-end commercial runway, not just a hackathon:

Stage	Program pillar	Partner benefit
1. Build	Seed credits	Offset upfront costs: Upfront scoped credits to offset build and porting costs, consumption-based performance credits as partners scale, and scoped engineering support to accelerate development.
2. Launch	Cloud marketplace	Enable discoverability: Publish directly to Google Cloud Marketplace with streamlined billing, procurement, and enterprise integration to gain direct visibility across global enterprise and mid-market accounts.
3. Scale	Co-marketing & GTM	Amplify market demand: Access custom co-marketing and launch assets, social promotion, PR kits, launch spotlight campaigns, and Google-funded campaign packages for top-performing partners.

Enterprise deployment architecture: Regardless of whether a partner builds a Skill, Plugin, MCP server, or A2A agent, the architecture is designed to keep customer onboarding frictionless and partner IP secure. Gemini users access the partner integration in Gemini using native OAuth, Dynamic Client Registration (DCR), and streamlined Bring Your Own License (BYOL) flows, while the partner maintains backend security, data sources, and proprietary tooling within their own project perimeter.
Built-in discoverability: Partner solutions are published directly to Google Cloud Marketplace and featured in AI Solution Finder, giving enterprise customers a central place to discover validated MCP connectors, plugins, and agents today—and making it simple for IT administrators to configure and govern them in Gemini.
Comprehensive incentives & go-to-market: Google Cloud supports verified partners through every growth stage. Qualified solutions unlock Seed Credits upon Marketplace publishing and performance credits to offset operational inference costs, paired with GTM assets and sales enablement to turn engineering milestones into a net-new pipeline.

Featured partners: proven speed to impact

Recent Gemini factory (previously referred to as the Agent Factory) cohorts have already brought a wave of industry-specific agents to life. Here is how nine software partners took complex enterprise challenges and deployed production-grade solutions built with Gemini in days:

Aily Labs

Aily Decision Intelligence Agent

The Aily Decision Intelligence Agent unifies 360° enterprise performance metrics to deliver predictive business diagnostics and actionable strategic recommendations. Built for executive decision-makers, it interrogates complex operational and financial datasets to forecast performance trends, identify supply disruptions, and guide strategic resource allocation through an intuitive, mobile-first interface.

“Our mission is to bring real-time, AI-powered decision intelligence to every business executive. The Agent Factory workshop was an incredible catalyst; it enabled our team to wire up complex predictive reasoning models into the Gemini Enterprise runtime, activating Aily Super Agent in record time; orchestrating thousands of specialized agents, connecting insights across multiple business domains, and presenting them through a single, intuitive interface for the user. Having side-by-side engineering support helped us prove out high-impact executive scenarios and accelerate our roadmap toward a verified listing on Google Cloud Marketplace.”

— Reynaldo Espana, VP of Engineering, Aily Labs

FlowX

Capital Markets Document Reconciliation Agent

FlowX's Capital Markets Document Reconciliation Agent streamlines high-stakes financial operations, including institutional document extraction, multi-party reconciliation, and loan pack completeness audits. Leveraging specialized document intelligence and Kubernetes-backed agent runtimes, it cross-checks disparate contracts, regulatory filings, and credit documentation to flag discrepancies in real time, drastically reducing cycle times for back-office operations.

“In capital markets and lending, precision, auditability, and data security are non-negotiable. What impressed us most about the Agent Factory was how rapidly we could implement complex business validation rules and multi-source document ingestion without compromising on enterprise compliance. Transitioning from raw use cases to a functional prototype in a single workshop demonstrated the sheer execution velocity possible on Gemini.”

— Serban Chiricescu, Founder & Chief Technology Officer, FlowX.AI

Klarvant

Digital Governance Analyst Agent

The Analyst Agent by Klarvant provides outside-in discovery and analysis of an enterprise’s external namespace: post-quantum readiness, certificate and TLS hygiene, email security, supply-chain risk, and external exposure. It tracks post-quantum migration endpoint by endpoint, matches supplier and dependency events to the points where they touch the estate, and answers plain-language questions with audit-ready evidence and custom dashboards built on request.

“What Gemini does exceptionally well is reason over complex technical evidence and explain it in plain language. That is exactly what governance needs. A board member or even a CISO does not want a list of ciphers; they want to know whether the company is ready and what to do next. With the Agent Factory, Gemini became the bridge between our intelligence and the people accountable for it.”

— Endre Fehn, Chief Technology Officer, Klarvant

NextLM

AI Prospecting Agent

The NextLM AI Prospecting Agent brings tenant-native prospect intelligence into Gemini. Powered by the Savant model family, it ranks the B2B accounts and buyers most likely to convert using behavioral data across 300M+ companies, 370M+ professionals, and 1.3B+ tracked news articles. Each customer gets an isolated model that is further trained on its own closed-won and closed-lost data, so prioritization reflects what actually wins for that business. Built to work alongside other agents in Gemini, it hands ranked accounts and buyer context to research, outreach, and CRM connectors, turning “who should we pursue and why now” into action across the workflow.

“Building autonomous B2B sales agents requires blending live external market signals with deep enterprise CRM context. The Agent Factory eliminated the typical development friction—our engineers used the RAD framework to go from an initial architecture diagram to an active, working prospecting agent in under 24 hours. The hands-on co-engineering with Google Cloud gave us the confidence and tooling to bring a production-ready solution straight into the Gemini Enterprise ecosystem.”

— Chris Anzalone, Founder & CEO, NextLM

Optimizely

Mark Marketing Calendar Deck Builder Agent

Optimizely Mark Marketing Calendar Deck Builder Agent converts cross-functional marketing strategies, campaign timelines, and experimentation roadmaps into executive-ready slide decks and visual briefs. Grounded in campaign performance metrics and content repository data, it enables marketing leaders to generate unified omnichannel schedules, align global teams, and track milestone delivery in seconds.

“Marketing teams spend countless hours manually consolidating disjointed campaign plans and roadmaps into presentations for leadership. The Agent Factory allowed us to harness Gemini and Vertex AI Vector Search to turn that manual chore into an autonomous, one-click experience. In just one intensive day, our team built an agent that unlocks immediate productivity gains for enterprise marketers and seamlessly plugs into our broader Mark platform.”

— Kevin Li, SVP Product, Optimizely

Ping Identity

PingID Device Management Agent

Employees manage their own MFA devices just by asking. When someone gets a new phone, the agent displays a live PingID pairing QR code with real-time status directly in Gemini, rendered with A2UI. Employees can also list, rename, set a default, or remove devices without opening a portal or filing a help desk ticket. That replaces a help desk call, and the over-the-phone identity check that social engineering attacks exploit, with a short conversation.

“Every new phone and every last day at work is an identity event, and too many of them still turn into help desk tickets. Working side by side with Google Cloud in the Agent Factory, we built agents that let employees manage their own MFA devices and let admins resolve identity requests in one conversation. With ADK, A2A, and A2UI, those experiences run natively in Gemini, and every action stays scoped to the user, confirmed by a person, and accountable.”

— Loren Russon, SVP, Product & Technology, Ping Identity

Sinch

Sinch RCS Messaging Agent

The Sinch RCS Messaging Agent enables enterprises to create and manage RCS business agents, draft interactive messaging campaigns directly from natural-language prompts, execute rich RCS broadcasts, and monitor delivery reports, built with Gemini and Sinch's carrier infrastructure.

“Collaborating with the Agent Factory team dramatically accelerated our timeline from prototype to a verified listing on Google Cloud Marketplace. Leveraging their structured environment and pre-built RAD skills, our engineering team was able to rapidly assemble, secure, and validate a complete lifecycle agent for RCS, covering enterprise authentication, RCS agent provisioning, prompt-based campaign drafting, and real-time delivery reporting. We walked away with a production-ready solution that harnesses Google Cloud’s ecosystem to help businesses deliver richer, more interactive conversational experiences at scale.”

— Antoine Sein, Senior Engineering Manager, Developer Experience & Agent Experience, Sinch

THG Ingenuity

Shopping Assistant Insights Agent

Shopping Assistant Insights converts shopper conversations into actionable commercial insights and plain-business answers grounded in BigQuery analytics. It tracks shopper demand, assistant performance, and customer friction while maintaining strict data privacy. Designed for commercial leaders, it highlights opportunities to stock, promote, or fix based on real user interactions.

“Our AI Shopping Assistant captures a depth of shopper intent that traditional search bars can't. The Agent Factory helped us turn this insight into valuable action points for brands and retailers. We leveraged Gemini to rapidly build a Shopping Insights Agent that turns raw consumer data into actionable business strategy. The collaborative environment accelerated our development, giving us a deeper understanding of the Agent Marketplace while delivering a live, functional agent that provides immediate value to our customers.”

— Jo Drake, CTO, THG Ingenuity

Xiatech

Xfuze Order Intake Agent

The Xfuze Order Intake Agent automates multichannel commerce order processing, exception triage, and inventory reconciliation across disparate enterprise systems. Powered by Xiatech’s Xfuze integration platform and BigQuery analytics, it continuously identifies supply chain anomalies, resolves fulfillment errors, and provides commercial operators with conversational visibility into real-time order-to-cash health.

“Connecting disparate legacy systems, e-commerce storefronts, and ERPs is a major hurdle for retail and supply chain leaders. At the Agent Factory, we leveraged the Antigravity IDE and RAD developer skills to build an agent that unifies these complex data streams effortlessly. The speed of execution was remarkable, in 24 hours, we built a first of its kind agentic application (multiple agents in one) that delivers instant operational transparency and tangible cost savings to our clients.”

— Jonathan Summerfield, Founder & Chief Executive Officer, Xiatech

The road ahead

The transition to the agentic era requires more than standalone applications—it demands an open, interoperable enterprise ecosystem. Through the Gemini factory, Google Cloud is bringing together technical velocity and commercial scale so partners can meet enterprises wherever they work:

End-to-end co-engineering: Rapid architecture design, AI-assisted development, and automated readiness testing in Antigravity alongside Google Cloud partner engineers.
Full-spectrum extensibility: Flexible blueprints in Gemini across deterministic A2A agents, standardized MCP connectors, declarative skills, and turnkey plugins.
Marketplace discovery and scale: Streamlined publishing to Google Cloud Marketplace, backed by seed credits, performance incentives, and joint go-to-market support.

By collapsing the journey from initial whiteboard concept to a verified Google Cloud Marketplace listing into a matter of days, the Gemini factory is helping partners bring the next generation of enterprise AI solutions to market faster than ever—and this is just the beginning. Explore partner-built agents, MCP connectors, skills, and plugins in the Google Cloud Marketplace and AI Solution Finder, or connect with your Google Cloud Partner team to start building in the Gemini factory.

Don't just flag it, fix it: How to run CodeMender in your pull requests

Tue, 10 Nov 2026 17:00:00 +0000

As many developers use agentic tools to create more code faster than ever before, the code has gotten more complex and they’re taking longer to find and fix software flaws.

Helping to address that pressure is a key concern at Google. We know that software development velocity is increasing, as much as 2x for some teams, and the average developer fix time for software flaws expanded from approximately 59 days a decade ago to more than 170 days in 2025, according to the Mandiant M-Trends 2026 Report.

To help you secure more code, more quickly, we’ve built CodeMender, our autonomous code-security agent that can find and fix software flaws at machine speed.

We also know that developer tools only succeed if they fit into existing routines. That’s why CodeMender, part of our AI Threat Defense platform, brings automated code fixes directly into the developer workflow. Today, we’re sharing this reference architecture that wires automated CodeMender remediation straight into Git pull requests via Google Cloud Run.

What the developer sees

Everything happens in the timeline. The scan posts one comment, a table of findings, each finding with an ID, and the two things you can type next.

Figure 1: A pull request triggers the scan on Cloud Run. Findings, exploit traces, and proposed patches all come back to the same timeline.

Reply /verify 612424da and the exploit attempt comes back in the thread, folded into a collapsible View Exploit Steps & Response Trace block so it doesn't bury the conversation.

Reply /fix 612424da and the patch arrives the same way, behind Preview Proposed Code Diff, with a draft pull request already open /approve there. Both commands take all in place of an ID.

Figure 2: The initial scan comment provides a table of findings and the actionable slash commands, keeping the developer entirely within the pull request timeline.

Nothing asks the developer to leave the pull request or learn a console.

Ship evidence, not just a severity

CodeMender cm find runs return a confidence score on the vulnerability finding during the code analysis. If the confidence score is below 100%, you can use cm verify to have CodeMender build and simulate an exploit in a sandbox to determine if the vulnerability is actually exploitable.

The simulation results can change the severity. Because the verification sandbox is ephemeral, the pipeline automatically streams these artifacts to Google Cloud Storage to preserve an audit trail for compliance and security operations reviews.

Figure 3: The /verify command returns a trace of the sandboxed exploit attempt, giving reviewers actionable evidence rather than abstract alerts.

Engineer for failure modes in your verification pipeline. If your harness only searches for a successful exploit string, then any test failure — such as a crashed mock server or an execution timeout — can cause the pipeline to fail open and mistakenly label a real vulnerability as safe.

Ensure your parser separates true negative results from test crashes. If an exploit fails to complete cleanly, flag the status as inconclusive rather than giving it a clean bill of health.

Propose the patch, preview the diff

When an exploit proves a finding is real, the next step isn't a ticket, it's a patch: cm fix a1b2c3d4 instructs CodeMender to author one.

Figure 4: The /fix command returns a unified code diff, giving reviewers meaningful context to evaluate the patch.

If the diff looks good, applying it takes one step. The developer replies /approve. The agent commits the patch to the branch, pushes it, and sets a commit status security/codemender-gate to green. Branch protection is what turns that status into a merge block; without it, the gate is advisory.

No stored keys between GitHub and Cloud Run

None of this happens until GitHub and Google Cloud trust each other. This agent needs write access to your source code, precisely the credential you don't want sitting in a secret store waiting to leak. GitHub Actions can mint a short-lived OIDC token instead, and Google can be configured to accept it, so nothing long-lived exists to steal.

Nearly all the security here rests on one line:

code_block: <ListValue: [StructValue([('code', 'resource "google_iam_workload_identity_pool_provider" "github" {\r\n workload_identity_pool_provider_id = "github-provider"\r\n\r\n oidc {\r\n issuer_uri = "https://token.actions.githubusercontent.com"\r\n }\r\n\r\n attribute_mapping = {\r\n "google.subject" = "assertion.sub"\r\n "attribute.repository_id" = "assertion.repository_id"\r\n }\r\n\r\n # Without this, any repository on GitHub can exchange a token against your pool\r\n attribute_condition = "assertion.repository_id == \'123456789\'"\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea8d1a710>)])]>

Every GitHub Actions workflow shares that issuer URL, so validating the issuer alone buys you nothing. The condition is the part doing the work.

Note what it matches on. assertion.repository would also work here, but it matches a name and names get renamed, or claimed by someone else once you release them. repository_id and repository_owner_id are immutable, which is why Google recommends mapping those instead.

Give the exploit somewhere safe to run

Verification runs attack code that nobody reviewed, written on demand, pointed at a mock target, doing what the vulnerability class implies. As recent headlines have reminded everyone, the last thing you want is an autonomous agent deciding the fastest way to complete a task is to break out of its container and port-scan your internal network.

Before you can trust the verdict, you have to answer two important questions: Where does that code execute, and what can it reach while it does? The answer is to run it as a Cloud Run job.

Every Cloud Run instance is sandboxed, and jobs always run in the second-generation execution environment, a microVM with full Linux compatibility. The execution starts, does one thing, and exits.

Figure 5: The exploit runs in an instance that holds nothing and lives for one finding. Only the verdict leaves.

Then empty the room. Give the job a service account of its own and grant it nothing beyond the API that writes the exploit. It needs no registry access, because Cloud Run imports the container image when you deploy rather than pulling it at the instance start.

Skip the Compute Engine default account, which may carry roles/editor depending on your organization policy. Take the network away too: Direct VPC egress into a subnet with no route out means code that can't call anywhere can't exfiltrate what it finds or fetch a second stage.

Run one job per finding. The execution ends, the instance goes with it, and the verdict is the only thing that outlives either.

Cloud Run sandboxes or agent sandboxes close the last gap. In the instance, process execution is isolated from the parent workload, its environment variables, its secrets, and the Google Cloud metadata server, so the credential the platform hands out by default is out of reach as well. It's built for agents running code they just wrote.

Get started today

You can build an autonomous GitOps security agent today. Start with the reference implementation at codemender-gitops.

BigQuery measures is now GA: Unifying BI metrics and knowledge graphs for AI accuracy

Tue, 10 Nov 2026 17:00:00 +0000

To answer a complex business question, an AI agent must understand two things: what happened, and why. The what is a governed metric, like revenue at risk; the why is the network of relationships that produced it. Because metric definitions, a.k.a. measures, traditionally live in business intelligence (BI) platforms while relationships live in knowledge graphs, an AI agent only ever sees half the picture. It guesses the rest, leading to confident but sometimes wrong answers. To solve this hallucination problem, we introduced measures in BigQuery, which are now generally available.

Historically, organizations needing both BI and knowledge graphs had to run two siloed technology stacks: one to calculate reliable metrics, the other to map out entity relationships. Bridging them entailed brittle data pipelines and runtime reconciliation by analysts and AI agents, who held the connections in their heads or in sprawling prompts. This patchwork approach wasn't just slow and expensive — it’s where data governance broke down, as every analyst and prompt may define joins and metrics differently.

Figure 1: A unified model for defining BI metrics and knowledge graphs in one place, for powering AI agents, SQL, GQL and dashboards.

To replace manual reconciliation, you need a single model that understands both the structure of your business and the math that measures it. BigQuery measures builds directly on BigQuery Graph to deliver that unified foundation. By defining measures directly on the graph entities they describe, your BI dashboards and AI agents don't just get a number they can trust — they get the relational context behind it. Finally, they can reliably answer both the what and the why.

"With BigQuery measures, we can now deliver self-documenting data products. Defining measures natively in BigQuery ensures Looker Dashboards, Conversational Agents and our Data team queries operate on identical logic, guaranteeing metric alignment across leadership and significantly reducing data team maintenance overhead." - Amanda Reelick, Head of Data Enablement, Woolworths NZ

One model to answer both the what and the why

Let's walk through an example. Suppose your retail bank's risk analytics team spots a sudden spike in transaction activity in the Prague district. You need to know whether this represents healthy customer growth or concentrated risk exposure. Answering requires two pieces of evidence: how much money actually moved (the what), and which specific accounts it flowed through (the why). At most banks, getting that answer takes two teams, two tools, and two numbers that are rarely reconciled.

With BigQuery, your underlying data is already in place — whether in native tables or in a borderless Lakehouse. With a single schema, you can define a semantic model directly over your existing transaction, account, and district tables — with no data movement and no secondary system:

code_block: <ListValue: [StructValue([('code', '-- Entities, relationships, and measures, declared in one model\r\nCREATE OR REPLACE PROPERTY GRAPH retail_banking.BankingGraph\r\n NODE TABLES (\r\n retail_banking.trans AS Trans\r\n KEY (trans_id)\r\n PROPERTIES (\r\n trans_id, account_id, amount,\r\n MEASURE(SUM(amount)) AS total_trans_amount\r\n ),\r\n retail_banking.account AS Account\r\n KEY (account_id)\r\n PROPERTIES (\r\n account_id, district_id, stmt_frq,\r\n MEASURE(COUNT(account_id)) AS account_count\r\n ),\r\n retail_banking.district AS District\r\n KEY (district_id)\r\n PROPERTIES (district_id, city, state)\r\n )\r\n EDGE TABLES (\r\n retail_banking.trans AS POSTED_TO\r\n KEY (trans_id)\r\n SOURCE KEY (trans_id) REFERENCES Trans (trans_id)\r\n DESTINATION KEY (account_id) REFERENCES Account (account_id)\r\n NO PROPERTIES,\r\n retail_banking.account AS LOCATED_IN\r\n KEY (account_id)\r\n SOURCE KEY (account_id) REFERENCES Account (account_id)\r\n DESTINATION KEY (district_id) REFERENCES District (district_id)\r\n NO PROPERTIES\r\n );'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea8d1c550>)])]>

To determine whether the Prague spike reflects broad growth or concentrated exposure, your team checks whether the transaction volume is spread across many accounts or driven by just a few. Using GRAPH_EXPAND, you flatten the graph and roll up both measures with the AGG() function:

code_block: <ListValue: [StructValue([('code', '-- Volume by city, and the accounts behind it, each counted once\r\nSELECT\r\n District_city,\r\n AGG(Trans_total_trans_amount) AS total_volume,\r\n AGG(Account_account_count) AS accounts_involved\r\nFROM GRAPH_EXPAND("retail_banking.BankingGraph")\r\nGROUP BY District_city;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea867f150>)])]>

You can ensure accurate reporting by using measures that have been “grain-locked” — where a fixed level of granularity has been assigned to how facts and dimensions are modeled. Because a single account can have thousands of transactions, joining the two tables can duplicate account rows. A standard SQL COUNT(account_id) would overcount those accounts, leaving your risk and finance teams with conflicting numbers. BigQuery measures preserve the native grain of each entity, counting every account only once regardless of how the tables are joined.

The second half of the investigation is about connectivity: Which accounts are driving the Prague activity? You can answer that with a traversal in GQL (the ISO-standard Graph Query Language) over the exact same model:

code_block: <ListValue: [StructValue([('code', "-- Which accounts moved money in Prague\r\nGRAPH retail_banking.BankingGraph\r\nMATCH (:Trans)-[:POSTED_TO]->(a:Account)-[:LOCATED_IN]->(d:District)\r\nWHERE d.city = 'Prague'\r\nRETURN DISTINCT a.account_id"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea867f450>)])]>

Neither query requires a separate system, and both evaluate against the same governed definitions.

New features in BigQuery measures

While the preview introduced grain-locked measures tightly integrated with BigQuery Graph, real-world production analytics demand more than basic sums — and enterprise AI agents need programmatic ways to discover and interpret those metrics. The GA of BigQuery measures focuses on two key areas: richer modeling and visualization for complex business logic, and native semantic discovery for the agentic ecosystem.

Richer modeling, querying and BI visualization

To enable more expressive and flexible measure definitions, MEASURE() supports both composite expressions and derived measures; this capability is GA. Rather than repeating conditional logic across queries, you can define baseline aggregates once and compose them into higher-order calculations on the same table using AGG(). BigQuery validates these formula dependencies at schema compilation time to catch circular references or invalid aggregations early. Downstream dashboards and AI agents can then query complex ratios and net metrics at any dimensionality with consistent accuracy.

For example, on your Trans node, you can define settled volume alongside total volume, and then derive unsettled volume and unsettled percentage directly from those building blocks:

code_block: <ListValue: [StructValue([('code', '-- Compose custom calculations, conditional expressions, and derived ratios\r\nPROPERTIES (\r\n trans_id, account_id, amount, fee, is_settled,\r\n MEASURE(SUM(amount)) AS total_trans_amount,\r\n MEASURE(SUM(CASE WHEN is_settled = TRUE THEN amount ELSE 0.0 END))\r\n AS settled_volume,\r\n MEASURE(AVG(CASE WHEN amount > 10000 THEN amount ELSE NULL END))\r\n AS avg_large_trans,\r\n -- Derived: written in terms of the measures above\r\n MEASURE(AGG(total_trans_amount) - AGG(settled_volume)) AS unsettled_volume,\r\n MEASURE(\r\n SAFE_DIVIDE(AGG(total_trans_amount) - AGG(settled_volume),\r\n NULLIF(AGG(total_trans_amount), 0)) * 100.0\r\n ) AS unsettled_pct\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea8b42090>)])]>

Query across multiple fact tables

Enterprise data models routinely span multiple business processes. Joining separate fact tables that share a common dimension — such as transactions and credit cards linked to the same bank account — traditionally causes a "chasm trap," where cross-fact multiplication inflates both metrics.

The new start_node argument in GRAPH_EXPAND solves this by isolating each fact table into its own expansion tree. BigQuery evaluates each measure at its native grain before joining the results on the shared entity:

code_block: <ListValue: [StructValue([('code', '-- Transactions and cards per account, each evaluated at its own root grain\r\nSELECT\r\n ET.Account_account_id,\r\n AGG(ET.Trans_settled_volume) AS settled_volume,\r\n AGG(EC.Card_card_count) AS total_cards_issued\r\nFROM\r\n GRAPH_EXPAND("retail_banking.BankingGraph", start_node => "Trans") AS ET\r\nLEFT JOIN\r\n GRAPH_EXPAND("retail_banking.BankingGraph", start_node => "Card") AS EC\r\nON ET.Account_account_id = EC.Account_account_id\r\nGROUP BY ET.Account_account_id;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea89d60d0>)])]>

By expanding each fact table from its own root (Trans and Card), each branch maintains its exact grain — even for accounts with transactions but no issued cards.

Visualize in Data Studio

With the updated BigQuery connector in Data Studio, you can now connect directly to BigQuery graphs and build interactive visualizations powered by native measures. Data Studio honors the aggregation logic defined in your graph model, keeping complex calculations consistent across charts while preventing accidental client-side re-aggregations.

Built for the agentic era

Attach business meaning to your measures

In a large enterprise schema, it’s nice to be able to attach business meaning to your measures, as multiple metrics often share similar names. In our banking model alone, total_trans_amount, settled_volume, and unsettled_volume could all casually be called "volume" by a business user. That ambiguity can trip up both analysts building reports and AI agents translating natural-language prompts into SQL.

With the GA, you can embed rich semantic metadata directly into each measure definition using the OPTIONS clause, including plain-language description strings, domain synonyms, and custom key-value extensions:

code_block: <ListValue: [StructValue([('code', "-- Business meaning and governance metadata travel with the metric\r\nMEASURE(SUM(CASE WHEN is_settled = TRUE THEN amount ELSE 0.0 END)) \r\n AS settled_volume\r\n OPTIONS (\r\n description = 'Transaction value that settled. Excludes fees.',\r\n synonyms = ['settled amount', 'cleared volume'],\r\n extensions = [('unit', 'USD'),\r\n ('owner', 'finance-analytics'),\r\n ('reporting_tier', 'executive')]\r\n )"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea96f27d0>)])]>

Because this business context lives inside the schema alongside the SQL formula, your semantic definitions travel wherever the metric is queried.

Make measures discoverable to humans and AI agents

BigQuery directly integrates with Knowledge Catalog to create a unified discovery and consumption loop for both people and machines:

Enterprise-wide context discovery in Knowledge Catalog: BigQuery Graph and measures metadata is automatically ingested and searchable in Knowledge Catalog as a native resource alongside tables, views, and routines. Data stewards, analysts, and AI agents can search across the catalog using business synonyms or descriptions, inspect governance tags, and view the underlying measure’s SQL definitions (such as SUM(CASE WHEN is_settled = TRUE...)) to verify how each metric is calculated before using it. In Knowledge Catalog, you can also enrich graph metadata with business glossaries and stewardship context, uniting asset discovery, business meaning, and mathematical measures in a single pane so users and agents can work from the same context.
Runtime schema introspection in BigQuery: Calling BQ.SHOW_GRAPH_EXPAND_SCHEMA returns an expanded graph's schema as JSON without scanning underlying tables, flagging metrics with "is_measure": true alongside their descriptions and synonyms. Passing flatten_results => FALSE preserves your entity hierarchy as nested ROW (STRUCT) types, letting agents inspect the relational tree and query paths like Account.District.city directly.

Connect across your data and AI ecosystem

Finally, BigQuery measures integrates across the Google Cloud data and AI stack so you can author, reuse, and query semantics in the tools you already use:

Author with AI: Use the graph skills in Data Agent Kit to scaffold, author, and validate BigQuery measure definitions using natural-language prompts in your preferred AI coding environment.
Reuse existing LookML models: If you already model business metrics in Looker, you can model once using LookML-based derived analytic models to compile Explores, joins, and measures directly into native BigQuery Graph models.
Query with natural language: Unified graph and measure definitions are supported out-of-the-box in Conversational Analytics, enabling business users and AI agents to ask complex questions against governed metrics in plain natural language.

Get started with BigQuery measures

The primary advantage of this unified model is that you can build from the foundation you already have. If you already run a BI semantic layer, take a governed metric and define it directly on the entity it describes; if your foundation is a knowledge graph, attach a unified measure to your most frequent traversal. Whichever side you start from, you are building a single source of truth for both your BI dashboards and your AI agents.

To see unified semantics in action, watch the BigQuery measures introduction video, or dive into the BigQuery measures documentation and BigQuery Graph overview. When you are ready to build hands-on, try our step-by-step codelabs for music streaming analytics.

Experimenting with ClusterNetworkPolicy in GKE

Tue, 10 Nov 2026 16:00:00 +0000

Standard Network Policies limit you to applying policies at the namespace level, but ClusterNetworkPolicy (CNP) lets you apply granular networking policies across an entire cluster. When you use CNP, you can easily scale security and governance policies. Recently, I experimented with this functionality in Google Kubernetes Engine (GKE) and saw the benefits for myself.

In this post, we'll explore using CNP to control traffic to an LLM and cluster. If you want to jump right into a hands-on lab, check out this Codelab: Secure AI Inference Workloads with GKE ClusterNetworkPolicy.

How CNP works

CNP follows a three-tier flow with a precedence match:

Admin tier (top precedence): Cluster administrator enforces mandatory security rules that can't be bypassed (for example, blocking metadata access).
Network policy tier (middle precedence): Standard namespace-scoped policies that are managed by application teams to control ingress and egress for their microservices.
Baseline tier (bottom precedence): Cluster administrator establishes fallback defaults (for example, a default-deny posture across namespaces).

Design pattern example

The aim of the experiment in the Codelab is to restrict traffic to the LLM namespace, allow only authorized clients, and prevent cluster-wide access to the metadata API except for DNS port 53. It uses these services:

Virtual Private Cloud (VPC): Provides private networking, subnets for pods and services, and secure outbound egress via Cloud NAT.
TPU v5e: High-efficiency accelerator hardware (2x2 topology) running optimized model inference.
GKE: Managed Kubernetes platform that's powered by GKE Dataplane V2 (eBPF) to enforce multi-tier networking policies.
Gemma 4 (google/gemma-4-E2B-it): Lightweight open model served with vLLM to act as a sensitive AI workload.
ClusterNetworkPolicy (CNP): Cluster-wide policies that let administrators enforce mandatory administrator and baseline guardrails across all namespaces.
NetworkPolicies: Standard namespace-scoped rules that let developers grant access to authorized clients.
Cloud Logging / GKE Network Policy Logging: Captures real-time packet verdicts (allow/deny) and policy audit trails.

Putting it together: Setup overview

Setting up this environment begins with building a custom private network infrastructure and deploying your TPU-enabled clusters:

Custom VPC & Cloud NAT: Establish a custom VPC with a dedicated subnet that hosts primary nodes, plus secondary CIDR IP address ranges for GKE pods (10.20.0.0/16) and services (10.30.0.0/20). A Cloud Router and Cloud NAT are configured to handle secure outbound egress for nodes while blocking inbound internet exposure.
GKE Standard cluster: Spin up a Standard GKE cluster running GKE Dataplane V2 (ADVANCED_DATAPATH). GKE Dataplane V2 acts as the core engine that leverages eBPF under the hood to enforce both standard and cluster-scoped policies dynamically.
Dedicated TPU v5e node pool: Provision a node pool featuring ct5lp-hightpu-4t machines arranged in a 2x2 topology slice. The nodes carry a dedicated taint (google.com/tpu=present:NoSchedule) to ensure that only accelerator workloads run on them.
Deploying the Gemma 4 workload: Isolate the pods by creating three distinct namespaces:
- gemma-llm (tier: ai-core): Serves the lightweight Gemma 4 model using the high-performance vllm/vllm-tpu:latest engine on port 8000.
- client-ns (tier: app-frontend): Runs the authorized chat client that must query the model.
- untrusted-tenant (tier: untrusted): Represents an unauthorized probe container simulating a rogue client.

Securing the LLM and metadata: Code walkthrough

By default in Kubernetes, all cross-namespace and egress traffic is open. To enforce a defense-in-depth posture, we apply network policies across the GKE evaluation tiers.

Step 1: Establish zero-trust posture (baseline tier)

To deny all unauthorized ingress cluster-wide, the cluster administrator applies a ClusterNetworkPolicy at the baseline tier. This tier acts as a fallback safety net.

code_block: <ListValue: [StructValue([('code', 'apiVersion: policy.networking.k8s.io/v1alpha2\r\nkind: ClusterNetworkPolicy\r\nmetadata:\r\n name: baseline-default-deny\r\nspec:\r\n tier: Baseline\r\n priority: 1000\r\n subject:\r\n namespaces: {}\r\n ingress:\r\n - name: allow-dns-ingress\r\n action: Accept\r\n from:\r\n - namespaces: {}\r\n protocols:\r\n - udp:\r\n destinationPort:\r\n number: 53\r\n - tcp:\r\n destinationPort:\r\n number: 53\r\n - name: baseline-deny-cross-ns\r\n action: Deny\r\n from:\r\n - namespaces: {}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ec821c590>)])]>

How it works

Precedence placement: Setting tier: Baseline places this policy at the very bottom of the GKE evaluation chain, and the priority: 1000 value orders it within that tier. It only processes traffic that hasn't matched a rule in any higher tier.
Global target: Specifying subject.namespaces: {} ensures that this fallback applies globally to every pod in every namespace.
DNS isolation exemption: The first ingress rule accepts incoming DNS traffic on TCP and UDP port 53. Without this explicit exemption, cluster-wide DNS resolution would fail.
Fallback deny: The second rule matches any incoming pod-to-pod connection attempt and drops it. At this stage, both the client-ns and untrusted-tenant namespaces are blocked from contacting Gemma.

Step 2: Delegate access to developers (networkpolicy tier)

With the baseline default-deny active, the application team that manages gemma-llm can safely delegate access to legitimate clients. They define a standard namespace-scoped NetworkPolicy to allow incoming port 8000 traffic from rag-agent pods in the client-ns namespace.

code_block: <ListValue: [StructValue([('code', 'apiVersion: networking.k8s.io/v1\r\nkind: NetworkPolicy\r\nmetadata:\r\n name: allow-rag-client\r\n namespace: gemma-llm\r\nspec:\r\n podSelector:\r\n matchLabels:\r\n app: gemma-service\r\n policyTypes:\r\n - Ingress\r\n ingress:\r\n # Explicitly permit ingress only from pods matching app: rag-agent in client-ns\r\n - from:\r\n - namespaceSelector:\r\n matchLabels:\r\n kubernetes.io/metadata.name: client-ns\r\n podSelector:\r\n matchLabels:\r\n app: rag-agent\r\n ports:\r\n - protocol: TCP\r\n port: 8000'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ec83350d0>)])]>

How it works

Developer control: This resource resides within the gemma-llm namespace and it's managed directly by the developer, matching the GKE focus on operational separation.
Middle precedence matching: Standard Kubernetes NetworkPolicies map directly to the NetworkPolicy Tier. Because this tier is evaluated before the baseline tier, incoming TCP packets from the authorized client to port 8000 match this rule and are immediately accepted.
Fall-through block: Connections that originate from the untrusted-tenant namespace fail to match this rule, pass through to the baseline tier, and are dropped.

Step 3: Enforce global egress guardrails (admin tier)

By default, Pods can query the GKE link-local Compute Engine metadata server (169.254.169.254). Those queries expose instance metadata and potentially high-privilege service account tokens. To lock this down, the cluster administrator applies an egress guardrail at the highest tier in the GKE hierarchy.

code_block: <ListValue: [StructValue([('code', 'apiVersion: policy.networking.k8s.io/v1alpha2\r\nkind: ClusterNetworkPolicy\r\nmetadata:\r\n name: admin-global-guardrails\r\nspec:\r\n tier: Admin\r\n priority: 10\r\n subject:\r\n namespaces: {} # Targets every namespace in the cluster\r\n ingress:\r\n # Delegate all ingress decisions down to developer and baseline tiers\r\n - name: delegate-all-ingress\r\n action: Pass\r\n from:\r\n - namespaces: {}\r\n egress:\r\n # Allow DNS resolution so pods can locate endpoints\r\n - name: allow-all-dns\r\n action: Accept\r\n to:\r\n - networks:\r\n - 0.0.0.0/0\r\n protocols:\r\n - udp:\r\n destinationPort:\r\n number: 53\r\n - tcp:\r\n destinationPort:\r\n number: 53\r\n # Hard deny on the Compute Engine metadata server\r\n - name: block-metadata-server-global\r\n action: Deny\r\n to:\r\n - networks:\r\n - 169.254.169.254/32\r\n # Delegate remaining egress traffic to lower tiers\r\n - name: delegate-other-egress\r\n action: Pass\r\n to:\r\n - namespaces: {}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f5ea89d5850>)])]>

How it works

Admin tier precedence: Deploying this policy in the admin tier ensures that it's evaluated first, and priority 10 orders it within that tier. GKE processes this policy before standard NetworkPolicies and baseline rules.
Controlled delegation: The delegate-all-ingress and delegate-other-egress rules have an action: Pass directive that instructs the engine to forward evaluation down to the next tier for remaining in-cluster traffic.
Inviolable guardrail: When any pod attempts to query 169.254.169.254/32 on non-DNS ports, the query matches the block-metadata-server-global rule and it's dropped. Because this drop occurs at the top admin tier, it can't be bypassed or overridden, even if a namespace developer deploys an allow-all egress policy.

Verifying with Observability for GKE

Verifying and auditing security policies is straightforward using the built-in telemetry capabilities of GKE Dataplane V2:

Hubble eBPF streams: Provides real-time, kernel-level packet inspection without injecting sidecars. Administrators can stream live network flows to observe policy verdicts as they happen, making it easy to validate allowed communication paths and identify dropped packets immediately.
GKE Network Logging and Cloud Logging: Automatically exports structured connection telemetry and policy enforcement verdicts directly into Cloud Logging. Teams can filter and alert on policy decisions using attributes such as disposition="deny", source/destination namespaces, and jsonPayload.policies[0].name to maintain audit compliance and investigate blocked connection attempts.

CNP can be useful and, as always, when you use it in your production environment, you'll need to plan and design the rules based on your setup.

Next steps

Take a deeper dive and find more information on these features:

Want to ask a question, find out more, or share a thought? Connect with me on LinkedIn!

Introducing Gemini for healthcare

Tue, 10 Nov 2026 12:00:00 +0000

Every healthcare organization is working to deliver great care to more people at a lower cost. However, a general AI model is not enough to solve this challenge. Today, healthcare runs on fragmented data. Patient records, coverage policies, and clinical evidence remain trapped across disconnected records, claims systems, and administrative tools that don’t work together. Clinicians are forced to spend nearly 28 hours a week on manually reconciling information, and patients wait for answers about their care and their coverage.

Healthcare organizations need a single, secure, governed orchestration layer that coordinates work across the systems they already run, assembling verifiable evidence and taking multi-step workflows through to completion with human oversight.

Today, we are announcing Gemini for healthcare. This purpose-built industry solution extends the universal Gemini agent, we introduced last month at Gemini at Work.

The Gemini agent is a single, always-on agent for work where employees delegate an objective, and the agent plans the steps, retains organizational context, and routes across the best models to deliver finished outcomes inside the tools teams already use. The Gemini agent is built with enterprise-grade identity administration, fine-grained access control, and centralized policy management directly into its runtime. Every agent operates with a distinct identity, inherits each user’s existing role-based permissions, and enforces strict need-to-know boundaries across every user, sub-agent, and workflow.

Working across clinical, financial, and administrative systems, it orchestrates multi-step tasks without forcing teams to switch applications:

For providers: Gemini provides a patient’s history across care settings, charts are prepared before the visit, and authorization packets are assembled against payer policy before submission, so fewer claims come back denied.
For health plans and payers: Gemini ensures requests are checked against medical policy and organized for the clinical reviewer, and benefits and costs are explained to members from their own plan documents.

Gemini for healthcare is built to support HIPAA compliance, with governance running underneath it all. It’s available starting today in preview.

Gemini for healthcare empowers payers and providers to orchestrate complex clinical, operational, and administrative workflows.

Four components of Gemini for healthcare

Developed alongside clinical and health plan leaders, Gemini for healthcare provides an integrated, fully governed environment configured for payers and providers with four core components:

Skills for healthcare work: Skills are reusable instructions and context, written by domain experts, that teach Gemini to run a specific task. Purpose-built, multi-step instructions guide the Gemini agent through essential clinical and administrative tasks, such as medical records synthesis, pre-appointment preparation, coverage and policy review, and payer benefit determination. Each skill applies your organization’s policies and plan rules, and teams can build custom skills tailored to the workflows and standard operating procedures unique to their organization.
Connectors to clinical, financial, and operational systems: A library of connectors links Gemini directly to the EHR, claims, care management, and administrative systems organizations already run. Gemini queries each system while strictly preserving existing identity and role-based access controls, so teams get unified context without migrating sensitive data or altering permissions.
An open healthcare partner ecosystem: Every health system and health plan runs a different mix of systems. Healthcare systems integrators and digital health partners help organizations extend Gemini with specialized solutions, connect it to what they already run, and scale it without lock-in.
A secure, governed platform with open model choice: Clinical, security, and IT leaders get one place to set and enforce policies that govern every agent interaction. It is built to support HIPAA compliance, with enterprise-grade access controls, end-to-end audit logging, and verifiable citations so reviewers can check where each answer came from. Because the agent supports model choice, organizations can pair frontier models from Google and leading partners for complex clinical reasoning with fast, cost-efficient models for routine administrative steps under the same governance.

Unlocking high-value workflows with skills designed for payers and providers

Gemini for healthcare takes on the heavy assembly and administrative work that slows down clinical decisions and coverage determinations.

At the point of care:

Building a longitudinal timeline from fragmented sources: Synthesizes EHR and external records, separates active problems from resolved ones, lists allergies, and flags medication discrepancies. Clinicians get a grounded and unified patient picture in minutes rather than piecing together history across disconnected systems.
Reclaiming preparation time between patients: Prepares the patient chart ahead of a scheduled visit or procedure, flagging interval events, current medications, and outstanding clearances, labs, or consents, so care teams walk in with the full picture.
Streamlining clinical authorization and appeals: Extracts relevant chart evidence into an audit-ready packet for review, aligned with payer policy, flagging documentation gaps before submission rather than after a denial. If denials do occur, and the record supports it, it drafts an appeal grounded directly in the clinical record and the plan’s policy.

At the point of coverage:

Accelerating payer review: Checks incoming requests against the health plan’s medical policies and organizes the findings for reviewers, with findings traceable to the submitted documentation and coverage policy. Clinical reviewers spend less time assembling files and more time applying professional judgment.
Demystifying benefits and patient financial liability: Parses complex plan benefit booklets to estimate out-of-pocket costs, track deductibles, and verify in-network status, giving member services staff a step-by-step breakdown grounded in the member’s actual plan documents. For Medicare Advantage members, it applies Medicare’s coverage criteria where a plan’s internal rule is stricter.

Open ecosystem of connectors across the healthcare tech stack

Gemini for healthcare connects directly to core clinical, operational, and industry systems via secure connectors, inheriting each source system’s existing role-based access controls and data permissions.

EHRs and clinical data integration:

Epic EHR: Gives authorized users read-only access to patient conditions, medications, labs, clinical notes, and specialist documentation. With no write-back to the underlying chart, clinical data integrity remains completely intact.
Oracle Health EHR: Surfaces read-only patient histories, diagnostics, lab results, and clinical documentation for authorized users while enforcing zero write-back to ensure data integrity for the system of record.
InterSystems FHIR Server: Extending beyond EHR integrations, InterSystems FHIR MCP Server provides standardized, high-performance FHIR repository management and clinical data interoperability.
Verato: Resolves patient identity and matches records across visits and sources.
Keragon - Healthcare Integrations: Provides connectivity to 400+ healthcare applications, including EHRs, enabling organizations to securely connect their existing healthcare systems through a single MCP integration.

Eligibility, claims, and pricing:

Optum Coordination of Benefits: Finds additional patient coverage and determines the correct order of benefits before billing. Currently in limited release and testing with Gemini for Healthcare.
Optum Network Eligibility: Retrieves patient eligibility and benefit details from connected health plans in real time. Currently in limited release and testing with Gemini for Healthcare.
TriZetto Provider Solutions: Verifies patient eligibility, coverage, co-pays, and deductibles across Medicare, Medicaid, and major commercial plans via TriZetto's vast network.
GoodRx: Enables care teams to discover prescription pricing and savings options, including manufacturer-sponsored self-pay programs, directly within AI-powered workflows to help patients access more affordable medications.
Infinitus Relay: Automates high-volume payer phone calls required for prior authorization and benefit verification.

Terminology and coding:

IMO Health: Brings terminology normalization, clinical search, and knowledge graphs into healthcare skills and governed agentic workflows.
ICD-10 Codes: Searches and validates ICD-10-CM diagnosis codes, billable status, and hierarchy.
RxNorm Drug Vocabulary: Looks up standardized drug names, ingredients, strengths, dose forms, and NDCs.

Coverage and provider reference:

CMS Coverage: Connects directly to Medicare coverage rules, National and Local Coverage Determinations (NCDs/LCDs), ensuring coverage reasoning rests on current published policy rather than static model knowledge.
NPPES NPI Registry: Verifies provider identity, clinical taxonomy, and practice location in real time.

Evidence based guidelines:

Optum EvidenceVue: Turns real-world clinical data into actionable evidence through AI-powered workflows from Optum Life Sciences.
BMJ Digital Health: Delivers expert-curated, structured, peer-reviewed clinical guidelines and evidence, grounding care and coverage decisions in verifiable citations.

Clinical and operational workflows:

Salesforce: Headless Salesforce Health Cloud integrations bring workflows, data models, and Agentforce into Gemini for governed action right in the flow of work.
Suki: Delivers Ambient Clinical Intelligence across healthcare workflows, transforming clinical conversations into actionable intelligence that powers documentation, coding, revenue cycle management, and clinical reasoning.
UiPath: Connects healthcare-ready UiPath automations and agents to help teams assemble citation-backed context for operational reviews, prior authorizations, and appeals.
Synthpop Careflow: Orchestrates the patient journey across referral intake, coverage verification, and patient follow-up, turning unstructured referrals into validated, payer-ready orders without manual re-entry.
Alteryx: Enables healthcare teams to turn their domain expertise into automated analytics workflows, grounding AI-driven insights in business rules and calculations that are visible, understandable, repeatable, and auditable.

Enterprise productivity and collaboration:

Sana from Workday: Connects HR, finance, and enterprise knowledge so healthcare teams can surface policies, coordinate tasks, and get work done across administrative systems.
ServiceNow: Tracks IT incidents, operational service requests, and approval chains while enforcing existing enterprise routing rules.
Microsoft 365: Integrates directly with Word, Outlook, and SharePoint so prepared authorization packets, appeal letters, and benefit breakdowns land directly in the tools where teams already work.
Google Workspace: Connects seamlessly with Google Docs, Gmail, Drive, and Sheets to surface internal files and coordinate administrative workflows while enforcing enterprise access controls.

Security and compliance:

Cyera Dataport: Integrates with Cyera for data and agent security, delivering protected health information (PHI) context to support HIPAA audits and compliance reporting.

Global implementation partners and ecosystem

Through our open platform, organizations can deploy specialized partner agents such as ASCO Oncology Guideline Assistant or work with healthcare systems integrators, including 66degrees, Accenture, Altimetrik, AuxoAI, Cognizant, Deloitte, Egen, HCLTech, KPMG, NTT DATA, PwC, Quantiphi, Slalom, Tata Consultancy Services, TEKsystems Global Services, and Wipro, to customize, integrate, and scale these capabilities across complex enterprise architectures without vendor lock-in.

Optum Insight shares how Gemini for healthcare brings value to healthcare professionals at every stage.

Developed alongside leading healthcare organizations

Health systems, health plan providers, academic medical centers, and clinical leaders are already working with Google Cloud to shape and apply these capabilities across care delivery, plan operations, and medical research:

Synthesizing patient context at the point of care

“Making it easier for physicians to access and understand a patient’s medical history helps them spend less time gathering information and more time focused on patient care. Through our work with Google Cloud, we’re exploring how Gemini can interpret and synthesize complex patient information from multiple electronic health record systems and care settings, connecting data from different sources to give clinicians a more complete and cohesive understanding of a patient’s medical history.” — Michael J. Schlosser, MD, MBA, FAANS, Executive Vice President and Chief Clinical Officer, HCA Healthcare.
“Healthcare generates enormous amounts of information and data every day, yet patients often experience care as a series of separate interactions rather than one connected journey. Through our work with Google Cloud, we're exploring how AI can help connect information across the healthcare experience, making it easier to support the whole person, create more personalized care experiences, and equip clinicians with meaningful insights at the right time.” — Karen Hanlon, President, Highmark Health.
"By leveraging Gemini for healthcare, we will be able to use technology in a way that elevates the patient experience and strengthens the relationship between caregivers and those they serve. Through these integrated solutions, we expect to ease some of the administrative burden on our clinical teams and give them more time for what matters most: caring for their patients. Technology is at its best when it supports our caregivers and strengthens the human connection that is at the heart of healthcare." — Dr. John D'Angelo, President and CEO, Northwell Health.

Streamlining coverage, benefits, and payer operations

“The real promise of AI in healthcare isn’t simply doing today’s work faster. It’s creating a better way for the health system to work. When payers and providers can connect data, insights and workflows more seamlessly, we can remove unnecessary complexity, improve performance and help the entire system work better.” — Sandeep Dadlani, Chief Executive Officer, Optum Insight.
“Gemini for healthcare serves as a foundation for our unified architecture of reusable AI agents, enabling us to deliver new capabilities and productivity gains significantly faster than traditional development cycles. Our focus going forward is an agentic-AI model, where an intelligence layer drives deeper automation and operational transformation. Blue Cross Blue Shield of Massachusetts is working toward that future in healthcare with Google Cloud." — Prem Somasundaram, Chief Information Officer, Blue Cross Blue Shield of Massachusetts.

Accelerating clinical research and evidence-based guidelines

“For oncology care providers, quick access to current and relevant information is not a luxury, but a necessity. We are excited about what Gemini for healthcare could do to help care providers quickly find the critical information they need, when they need it, so they can drive better outcomes for patients.” — Clifford Hudis, MD, FACP, FASCO, Chief Executive Officer, American Society of Clinical Oncology.

Built on an enterprise-grade foundation

Google is both a frontier AI lab and an enterprise cloud. Gemini for healthcare runs on Google Cloud’s secure infrastructure and is built to support HIPAA compliance. Clinical data, prompts, and generated outputs remain within your organization’s governed environment and are never used to train or fine-tune Google’s foundation models. Outputs are grounded in clinical records, plan policies, and your organization’s approved guidelines and evidence, with traceable citations and audit logging that give clinical, compliance, and IT leaders visibility and control. And because we operate the full stack, from infrastructure and models through security and the application layer, we can optimize performance, reliability, and cost together, routing high-volume administrative tasks to fast, low-cost models and reserving high-reasoning models for complex clinical and operational analysis, so organizations can scale AI across the enterprise without scaling spend at the same rate.

This is just the beginning

The launch of Gemini for healthcare marks another step in our commitment to bring the best of Gemini to every professional and every workflow, natively tailored to the way their industry works.

Gemini for healthcare is available in preview in the U.S. today, joining our expanding suite of purpose-built solutions for legal, financial services, and government services. We invite leaders across health systems, medical groups, health plan providers, and payers to explore how Gemini can transform their most critical clinical and operational workflows.

The Blueprint: How Authentic Brands Group expands creativity with AuthOS

Tue, 10 Nov 2026 12:00:00 +0000

Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to inspire others looking to innovate in their work.

Authentic Brands Group, a global lifestyle and entertainment platform, is the owner of some of the most iconic intellectual property and cultural assets in the world.

Authentic’s portfolio spans more than 50 brands, from Reebok and Juicy Couture to Shaquille O’Neal and Elvis Presley, and reaches nearly one billion social media followers.The company accelerates its brands through a set of specialized businesses that combine powerful storytelling, premium content, unforgettable live experiences and global commerce.

The challenge:

Authentic’s platform requires a highly efficient exchange of information and deep coordination between its specialized business units, including entertainment, creative, marketing, live events, business development and more. The platform also requires ongoing communication and coordination with Authentic’s more than 1,700 licensees and strategic partners worldwide.

These demands are deeply interconnected: Marketing needs to deliver fast, brand-specific creative assets; creative teams must visualize new concepts in minutes, not weeks, and business development must research and prepare compelling proposals for potential partners. These teams work together to accelerate brand growth across categories and geographies. As the Authentic platform has grown, moving beyond traditional, siloed tools became essential to staying competitive.

Authentic needed an enterprise AI operating system that could support each brand’s unique voice and visual identity, provide instant access to proprietary business data, and help teams move faster without sacrificing quality or consistency.

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The solution:

To tackle this challenge, Authentic built AuthOS, an AI operating system powered by Gemini Enterprise Agent Platform and Cloud Run. Its 50 interconnected AI agents and tools help teams create compelling content faster, streamline workflows, and reduce time spent across disconnected tools.

AuthOS augments Google Cloud's large language models with unique Authentic brand and partner context and enterprise data stores. This allows teams to analyze business needs, brand knowledge and market intelligence to generate more relevant forecasts and recommendations.

For example, the Video Generator agent's multimodel pipeline utilizes Gemini 3.1 Flash and Gemini Omni to create cinematic starting and ending frames, Vision AI for detailed visual analysis, Gemini 3 for prompt optimization, and Veo 3.1 for final video generation. Together, these tools help maintain visual consistency and fidelity to each brand’s identity.

The Architecture:

The Outcome:

After launching our AI operating system we quickly saw how Vertex AI supported our AI workflow experimentation and iteration. We’re now able to incorporate any of the hundreds of models we want from Model Garden.

More than 80% of employees are using AuthOS weekly. We're thrilled with how the platform is benefiting our business, and are seeing tangible results. For example, AI-enhanced product images for Reebok, our largest brand, delivered more than 60% higher return on ad spend (ROAS) compared to unenhanced imagery.

AuthOS is also democratizing content production. Now, creative departments are joined by business development quickly creating pitch materials and marketing coordinators testing concepts without investing in full production.

Perhaps most important of all, Vertex AI's modular architecture makes it possible for Authentic to keep up with the ever-evolving AI landscape by integrating new tools almost immediately.

These capabilities give employees across Authentic new ways to create, collaborate and accelerate the growth of its brands worldwide.

The open AI-native stack on Kubernetes: From open models to distributed inference and agents

Tue, 10 Nov 2026 01:00:00 +0000

Over the past decade, Kubernetes established itself as the standard for orchestrating microservices and containerized applications. Today, we're witnessing the next major architectural shift in distributed computing: AI workloads moving from isolated research prototypes into large-scale, multi-tenant production fleets. In this post, we'll walk through the open-source stack that powers this shift and show you the sample manifests that you can try on your own Kubernetes clusters. You can also join us at KubeCon + CloudNativeCon North America 2026 to see these projects in action through live demos and technical sessions.

For modern engineering teams, production AI is rapidly moving beyond generic calls to closed, black-box APIs. Organizations are realizing that their long-term competitive advantage lies in domain specialization. Platform engineers, ML researchers, and AI builders want the autonomy to run high-performing open-weight models (such as the Gemma family). They want to adapt them to proprietary enterprise datasets by using reinforcement learning (RL) and serve them with predictable latency, high throughput, and maximum hardware efficiency.

Whether it's scientific initiatives training domain models like DolphinGemma (an LLM helping scientists study how dolphins communicate) on acoustic data, legal platforms evaluating complex contracts, or engineering teams building autonomous coding agents, the objective is the same: achieving frontier-level task performance at a fraction of the serving cost and latency.

Yet, scaling RL post-training, stateful distributed inference, and autonomous agent execution on traditional container infrastructure introduces substantial hurdles:

Accelerator underutilization in RL: When you alternate between training and rollout generation, expensive GPUs and TPUs remain idle for most of the training run.
Cache-blind load balancing: When you use standard Kubernetes round-robin and least-connections routing, multi-turn agent calls are scattered across arbitrary pods and wipe out key-value (KV) cache locality.
Framework fragmentation: When you operate parallel, siloed compute clusters (such as bespoke Ray or Slurm setups) alongside Kubernetes, you fragment capacity and multiply maintenance toil.
Agent security in multi-tenant clusters: When you execute untrusted, model-generated shell commands and scripts in shared environments, you expose clusters to sandbox escapes and privilege escalation.

At KubeCon + CloudNativeCon North America 2026, Google Cloud is addressing these challenges by introducing and expanding a unified, open-source AI-native stack across Kubernetes and Google Kubernetes Engine (GKE) based on these components:

OpenRL (CNCF Sandbox): A Kubernetes-native post training service that simplifies RL post-training and maximizes accelerator utilization.
llm-d 1.0 (CNCF Sandbox): An open distributed inference control plane that provides prefix-cache-aware routing, disaggregated serving, and multi-tier KV cache management.
Ray on GKE with KubeRay (CNCF Sandbox): A framework that provides enterprise lifecycle management, elastic autoscaling, and Kueue-driven scheduling for distributed Ray compute and data processing.
Agent Substrate (CNCF Sandbox): An open orchestration runtime that provides lifecycle management and execution targets for multi-turn AI agent workflows.
gVisor (CNCF Sandbox): A kernel-level container sandbox that delivers strong system call isolation when you execute untrusted agent code in multi-tenant clusters.
TorchTPU: A backend that provides first-class, native PyTorch support across Cloud TPUs and GPUs inside upstream vLLM and SGLang.

Bridge reinforcement learning and Kubernetes with OpenRL

While supervised fine-tuning (SFT) has become standard practice, domain excellence especially in multi-step reasoning, mathematical proofing, and autonomous tool use requires RL.

Historically, it's been difficult to bridge the world of ML experimentation with production Kubernetes fleets. ML researchers want to iterate rapidly in Python and PyTorch by using standard reward functions, while platform teams need multi-tenant governance, quota enforcement, and predictable scheduling.

To eliminate this friction, Google created OpenRL and contributed it to the CNCF Sandbox.

Architectural highlights of OpenRL

Clean 4-primitive Python abstraction: Researchers write standard Python training code that's centered around four core primitives (forward_backward, optim_step, sample, and save_state). OpenRL translates these primitives into Kubernetes scheduling workflows without requiring developers to learn Kubernetes internals or manage infrastructure code.
Cooperative accelerator time-slicing: OpenRL interleaves training steps and rollout inferences on the same physical accelerators. This approach avoids the need to dedicate separate, static clusters to compute-bound training and latency-sensitive rollout generation.
Delta-weight synchronization: Shipping full multi-gigabyte model weights over cluster networking after every optimization step creates massive network saturation. OpenRL transmits only parameter deltas to rollout workers, eliminating rollout cold starts.
Zero-config dynamic resource allocation (DRA): OpenRL automatically profiles model context lengths, batch dimensions, and LoRA versus full fine-tuning configurations. It then dynamically bin-packs workloads across a heterogenous accelerator fleet by using native Kubernetes DRA claims.

Distributed inference at scale with llm-d 1.0

After you post-train a model, serving it serving it under interactive, bursty agent traffic presents a new set of distributed systems challenges. Standard ingress controllers treat LLM traffic as stateless HTTP requests, scattering sequential turns across different replicas and constantly rebuilding KV cache.

Google Cloud, Red Hat, IBM Research, CoreWeave, and NVIDIA co-founded the project, with contributions from over 500 engineers across 25+ organizations. Today, llm-d 1.0 is the CNCF Sandbox distributed inference control plane that transforms standalone inference engines (like vLLM and SGLang) into a coordinated, cluster-wide serving fabric.

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Core capabilities of llm-d 1.0

Prefix-cache-aware routing (Endpoint Picker): The Endpoint Picker integrates into the Kubernetes Gateway API Inference Extension, and GKE manages it natively through GKE Inference Gateway. The Endpoint Picker evaluates real-time KV cache residency across worker pods. In production deployments, it's doubled KV cache hit rates from 35% to 70% and cut median time-to-first-token (TTFT) by 70-85% on multi-turn agent workloads.
3-Tier KV cache offload: When accelerator high-bandwidth memory (HBM) nears capacity during long-context interactions, llm-d cascades KV cache blocks from HBM → host CPU RAM → local NVMe SSD / Google Cloud Managed Lustre / Cloud Storage. This offload prevents expensive context recalculations.
EPD Disaggregation with DisaggregatedSet: Decouples Encoding, Prefill, and Decode (EPD) onto specialized hardware pools that are connected by high-speed interconnects. DisaggregatedSet is built built on Kubernetes LeaderWorkerSet (LWS). It enables independent horizontal scaling and coordinated, zero-downtime rolling upgrades.
Native TorchTPU upstream support: PyTorch models run natively on Google Cloud TPUs inside upstream vLLM and SGLang through TorchTPU. Developers get day-zero parity across TPU v5e, v6e, and v7x alongside NVIDIA GPUs without the need to maintain private forks or rewrite pipelines.
Latency-tolerant async processing & flow control: The llm-d-async-processor component pulls background, latency-tolerant tasks from Pub/Sub or Redis into cluster slack capacity. It applies strict traffic prioritization so background processing never degrades interactive user experiences.
Fleet observability with Prism: Track cluster-wide throughput, inter-token latency (ITL), TTFT, and cache hit ratios by using Prism, the llm-d open-source benchmarking and performance dashboard.

Distributed Python & ML orchestration with KubeRay

While specialized runtimes handle inference and RL loops, distributed data preparation, embedding generation, and distributed PyTorch training depend heavily on Ray.

KubeRay provides an open-source, Kubernetes-native operator that manages the complete lifecycle of Ray clusters, jobs, and services (RayCluster, RayJob, and RayService custom resources) on Kubernetes.

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Why KubeRay matters for platform teams

Declarative cluster lifecycle: Platform engineers can spin up ephemeral, self-healing Ray clusters on demand for ad-hoc training runs, or run long-lived clusters for batch pipelines.
Native Kueue queueing & multi-tenancy: KubeRay integrates directly with Kueue for multi-tenant fair sharing, priority preemption, and quota management across shared GPU and TPU capacity.
Elastic autoscaling: KubeRay dynamically scales worker pods based on active Ray tasks and actor placement, preventing idle resource consumption.

Strong isolation for AI agents: Agent Substrate & gVisor

As multi-turn AI agents advance from conversational assistants to autonomous systems that generate code, execute shell commands, and orchestrate external APIs running untrusted, model-generated code in shared clusters introduces serious security risks.

Google Cloud is contributing Agent Substrate and continuing core contributions to gVisor in the CNCF Sandbox:

Kernel-level sandboxing: gVisor provides a user-space kernel that intercepts and virtualizes system calls, which helps ensure that untrusted agent code can't compromise the host kernel or access neighboring tenant workloads.
Sub-second ephemeral execution: Sandboxes spin up in milliseconds per agent turn, providing isolated execution environments for both RL rollout generation in OpenRL and live production agent workflows.

Proven in practice: Mastering domain reasoning

To evaluate this complete open-source stack on a rigorous enterprise workload, we tested Gemma 4 against the open-source Legal Agent Benchmark (LAB) developed by Harvey.ai. This benchmark spans 24 complex legal practice areas from mergers and acquisitions (M&A)M&A due diligence to contract dispute analysis. The evaluation produced these results:

Benchmark results: While standard SFT plateaued around 50% accuracy on multi-step reasoning rubrics, when we applied reproducible RL loops with OpenRL, we lifted model accuracy to 70%.
Operational efficiency: The entire training pipeline dynamically shared GPU and TPU capacity with concurrent LoRA and inference jobs on GKE without manual partition management.

How teams are adopting this stack

Organization persona	Core challenge	Architecture with OpenRL + llm-d + KubeRay + Gemma
Enterprise platform teams	Manage multiple internal ML squads without fragmented Slurm/Ray clusters or runaway hardware bills.	Transform existing GKE clusters into self-hosted Reinforcement Learning-as-a-Service (RLaaS) by using DRA, Kueue, and accelerator time-slicing.
Regulated enterprises (finance, healthcare, legal)	Intellectual property (IP), compliance, and privacy mandates prohibit sending proprietary data to third-party public APIs.	Post-train and serve 1B-30B Gemma models completely inside a private Virtual Private Cloud (VPC) with domain-specific reward functions for superior task accuracy and lower total cost of ownership (TCO).
AI-native builders & agent developers	High serving costs and latency bottlenecks on long-context, multi-turn agent interactions.	Deploy llm-d prefix-cache routing that's paired with gVisor-isolated Agent Sandbox execution targets.
Model-as-a-Service & neocloud providers	Deliver high-margin, multi-tenant post-training and inference across diverse hardware fleets.	Expose OpenRL’s multi-tenant APIs and llm-d’s multi-accelerator control plane across mixed GPU and TPU clusters.

Connect with us at KubeCon North America 2026

Attending KubeCon NA in Salt Lake City? Come see these open-source technologies in action:

Join Google Cloud at KubeCon + CloudNativeCon North America 2026:

Come say hi to us at booth #122 in Hall B.
Discover Googlers talks on our Google Cloud at KubeCon North America 2026 event site.

Attend featured sessions & keynotes:

Kubernetes with Conviction: How Customization is Shaping the OSS AI Ecosystem's Future - Salt Palace | Level 1 | Hall DE.
The Latest in GPU, TPU, NIC and Other Device Support (WG Device Management) - Salt Palace | Level 2 | 250 D-F
Kubernetes Solutions for Agent-Shaped Problems - Salt Palace | Level 1 | Hall DE
How Anthropic Almost Melted GKE (And the PRs That Saved It) - Salt Palace | Level 1 | Ballroom H
Gateway API Project Update: Building the Future of Traffic - Salt Palace | Level 2 | 250 D-F

Watch live demos at the Google Cloud booth:

Demo | Inferencing with ARM processors on GKE (Tue 1:30-4:30PM MST)
Sandbox your agent workflows with gVisor (Tue 4:30-7:30PM MST)
Securely running fleets of agents with OpenShell (Wed 10:30-1:30PM MST)
Cost optimization via GKE spot nodes & node auto-provisioning (Wed 12:30-2:30PM MST)
From Reasoning to Action: Arm-Powered Coding Agents with GKE Agent Sandbox and Axion (Thu 10:30-1:30PM MST)

Get started

Explore OpenRL: Visit openrl.dev and check out the OpenRL GitHub repository for Kubernetes-native RL fine-tuning recipes.
Deploy llm-d 1.0: Head to llm-d.ai to test Gateway API Inference extensions and deploy prefix-aware inference pools on your clusters.
Run KubeRay: Follow the KubeRay documentation to manage distributed Ray workloads alongside Kueue.
Deploy Gemma on GKE: Explore step-by-step guides for deploying Gemma models on TPUs and GPUs in the Google Kubernetes Engine documentation.

New survey finds enterprises trapped between legacy software and AI modernization

Mon, 09 Nov 2026 17:00:00 +0000

Throughout my career in cybersecurity and infrastructure protection — both in government and now at Google Cloud — I have consistently seen that friction between new and existing technology is often a barrier to progress. A new piece of software or service may work wonderfully on its own but then it’s placed within a complex, interconnected IT environment and lost in the operational inertia.

Today, enterprise technology is experiencing a historic transformation. Organizations globally are moving beyond integrating AI and LLMs, and are now utilizing AI to power autonomous, agentic workflows. Nearly 90% of the Fortune 100 are already using Gemini Enterprise to build, scale and govern agents. But as AI systems begin to plan, execute multi-step processes, and work alongside human teams, leaders are confronting this same tension between cutting-edge and legacy technology.

To understand how existing technology environments shape an organization’s ability to operationalize AI, Google Cloud partnered with Hypothesis Group to survey 1,250 enterprise IT and business decision-makers across the United States. The findings reveal a critical paradox: the legacy software systems enterprises rely on to deliver technology to their employees are simultaneously functioning as an innovation tax, actively holding back the AI transformation leaders are trying to achieve.

Legacy IT’s operational toll

In our study, we defined “legacy” technology as software running locally on computers and servers rather than natively in the cloud. Across industries, decision-makers report that maintaining this footprint exerts a constant friction on their AI ambitions.

The consequences to AI are not theoretical. Nearly half (49%) of respondents report that relying on on-premises software has actively delayed, reduced in scope, paused or canceled AI or other technology initiatives.

Digging deeper, routine patch application and maintenance emerged as a primary operational bottleneck. As AI-enabled vulnerability discovery accelerates, the rate at which flaws are surfaced in legacy codebases has outpaced what human security teams can realistically address.

This creates a zero-sum game between routine maintenance of legacy systems and AI modernization. Instead of directing engineer cycles toward AI modernization, IT teams are spending their time and resources on maintaining legacy systems.

Enterprise monoculture

Why do organizations continue to rely on legacy environments that constrain their modernization? As our research shows, it is not necessarily architectural preference — but the result of vendor lock-in.

Beyond narrowing technology choice, deep provider dependence introduces commercial constraints. In our study, 39% of decision-makers report that reliance on legacy software and cloud providers forces them to navigate complex licensing rules or contracts, or limited negotiating flexibility.

Even more concerning is the systemic operational risk created by single-vendor concentration. Concentrating operating systems, productivity applications, cloud hosting and emerging AI models within a single vendor's monoculture creates an immense single point of failure. An autonomous AI agent is only as secure as the operating system and network it traverses. When that underlying stack is vulnerable, organizations are not deploying resilient intelligence; they are expanding their attack surface.

Customer choice

The solution to vendor lock-in is straightforward: enterprise buyers and policymakers must ensure organizations have genuine choice and interoperability in their software, cloud and AI architectures.

Furthermore, 80% of leaders agree that a provider’s installed base should not determine which AI technology an organization uses, while 78% believe that the providers who led the previous era of enterprise technology may not be best positioned to lead the AI era. Organizations want the freedom to choose best-of-breed intelligence on its own merits, rather than accepting bundled defaults tied to legacy desktop software.

Accelerate agentic technology

Delivering this level of autonomous agility across the enterprise requires modern technology built to support the agentic era. Here are a few ideas to get there:

Demand architectural and provider choice: AI and cloud must never be held hostage by the legacy software running local servers, email or desktop operating systems. Organizations need the commercial and architectural freedom to select best-of-breed IT on its own merits.
Commit to secure-by-design foundations: The industry must move past the broken cycle of releasing insecure software and patching it after the fact. Software manufacturers must engineer durable, resilient architectures where entire vulnerability classes are eliminated before code ever ships — relieving understaffed IT teams from perpetual applying routine but necessary patches.
Deploy AI-driven defense to remediate legacy debt: For the millions of lines of vulnerable legacy software already running in production, organizations must shift from manual triage to proactive, AI-assisted defense. By deploying platforms like Security Command Center and Mandiant threat analytics, teams can use intelligence to continuously surface active exploit paths, prioritize critical exposure and automate remediation across complex, hybrid environments.

The promise of agentic AI is to help automate friction away, and accelerate human ingenuity. But that future cannot be built on top of software designed decades ago. As IT leaders plan their roadmaps, it is time to ask: Is your technology foundation accelerating your future, or are you paying a maintenance tax just to keep the past running?

Learn more about the research here.

Cloud CISO Perspectives: Our security forecast for 2027

Mon, 09 Nov 2026 16:00:00 +0000

Welcome to the first Cloud CISO Perspectives for November 2026. Today, Chris Betz shares our security forecast for 2027, with insights from top leaders at Google, Google Cloud, and Mandiant.

As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and you’d like to receive the email version, you can subscribe here.

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Our security forecast for 2027

By Chris Betz, CISO, Google Cloud

Chris Betz, CISO, Google Cloud

Today, we’re publishing insights on the cybersecurity trends and challenges we anticipate in 2027, including agentic threats, changing attack tactics, and the new security defense approaches required.

This year we’ve witnessed a fundamental change in cybersecurity. When the year began, only a few organizations were using LLMs and agents to find and fix vulnerabilities. Today, it’s happening at scale across enterprises.

Organizations expected a reality with agentic hacking where scale, speed and sophistication would outpace human-only response; today, we’re seeing it in practice. Organizations predicted software development at an unheard of pace; today, we’re securing software delivered in hours and days instead of weeks and months.

We have passed many milestones, and the world won’t go back — and we are all digging in to fully understand the new reality.

The year ahead promises to be challenging, exciting, innovative and will outpace 2026. In the year ahead, enterprise software will change as agility and speed provided by agentic development shifts us from a collection of static applications to a live fabric of autonomous agents that can manage pricing, negotiate supplier contracts, allocate capital, and reroute supply chains in real time.

To make this vision a reality, many organizations are rushing to build agents. It’s on us as an industry to ensure the development and operations models are designed to be trustworthy and safe.

The risks in 2027 won’t be just server outages or stolen databases. As the agentic operations world takes off, we also need to be concerned about the data and technology foundation that agents rely on.

When an agent makes a consequential decision, you must be able to reconstruct the entire story: what inputs came in, how the agent reasoned through them, which tools it called, what parameters it passed, and why policy allowed it. Without that trace, you can't debug it, you can't validate its correctness, and you can't trust it. And if you can't trust it, you can't scale it. Karthik Narain
chief product and customer officer, Google Cloud

Security must evolve to protect the balance sheet through decision integrity. Every consequential action taken by an autonomous system requires a complete, tamper-proof decision trace, recording and tracking:

Inputs entered into the system.
How the model reasoned through its decisions.
Which tools the model called, what parameters it passed, and why policy allowed it.

Collecting this data will help ensure that your teams can reconstruct, validate, and audit every automated decision before scaling.

Making the alert queue disappear, from chasing tickets to setting doctrine

For decades, security operations centers (SOCs) have suffered from crippling alert fatigue — too many alerts compounded by legacy security tools that hand human analysts half-finished investigations. When autonomous threats can probe a network and move laterally in minutes, human defenders can’t manually stitch together logs across eight different consoles or approve individual defensive actions at microsecond speed.

2027 is the year analysts stop chasing alerts and start shaping outcomes. Ramya Chitrakar
vice-president, Engineering, Google Cloud Security

That broken alert queue disappears in 2027 by flipping the operational model. The alert becomes the final output of an investigation. Autonomous defensive agents will handle the data enrichment, correlation, and triage automatically, surfacing a verified conclusion rather than a raw ticket.

Instead of chasing alerts at a keyboard, human analysts move up the stack to set strategic doctrine, define risk appetite, and verify automated actions built for rapid explainability — where any defensive decision can be understood by a human in 60 seconds and reversed in 30.

Moving Beyond Zero: Transaction-level trust and first-class agent identities

As enterprises deploy thousands of custom agents across multicloud environments, flat trust architectures, shared human service accounts, and blanket administrator tokens are turning internal workflows into high-leverage entry points for prompt injection and lateral movement.

Traditional Zero Trust is too rigid and broad to govern autonomous software acting on its own.

We need to extend the Zero Trust concept, not just at the network layer and the machine layer, but now down to the transaction layer. Heather Adkins
vice-president, Security Engineering, Google

The year ahead demands an architectural shift to Beyond Zero so we can extend verification down to every individual transaction, network packet, and point-in-time authorization. Every autonomous agent should be treated as a first-class identity with its own verifiable credentials, strict least-privileged scope, and open communication standards.

When backed by real-time autonomous reasoning, we can dynamically allow a transaction on Tuesday and block the exact same request on Wednesday if conditions look anomalous.

Closing the loop between live runtime and defect-free code

The traditional divide between writing software and securing it is breaking down, while adversaries actively target the open-source libraries and external tools that coding assistants pull into codebases. Developers can no longer file a ticket and wait weeks for a manual security review, yet deploying hasty, unvalidated automated patches in production is just as dangerous to business continuity as the vulnerability itself.

Adversaries aren't just attacking model math and algorithms. They're actually poisoning the open source software, supply chain, MCP tools, and external libraries that agentic coding assistants pull automatically into code bases. Sandra Joyce
vice-president, Google Threat Intelligence

In 2027, security becomes a closed-loop, continuous system embedded directly into the software delivery pipeline. By placing agentic reasoning inside developer tools, we can perform threat modeling and catch configuration errors side-by-side as code is written.

It’s also crucial that we link live production environments directly back to source code so that defenders can trace a real-world exposure to the exact line of code, validate whether it is genuinely exploitable, and deliver a pre-tested fix at machine speed without breaking production.

Turning internal ground truth into a decisive defender advantage

Concerns persist around attackers gaining the upper hand in cybersecurity, and many fear autonomous capabilities will only widen that gap in 2027. Adversaries are already compressing six months of reconnaissance into hours, and exploiting flaws before vendors even know they exist. Compounding the challenge for defenders, no single model can discover or patch every vulnerability across code and cloud on its own.

The defender's advantage is that attackers operate with fragmented external visibility and guesswork. Defenders own the complete ground truth, identity privilege graphs, internal network architecture, code lineage, and real-time runtime activity. Sandra Joyce
vice-president, Google Threat Intelligence

In the year ahead, defenders will use autonomous technology to capitalize on the one thing an outside attacker will never have: complete internal ground truth. While adversaries operate with fragmented external visibility and guesswork, defenders own the full picture of their identity privilege graphs, internal network architecture, code lineage, and live runtime activity.

By feeding that unified context into multi-model ensembles — such as pairing lightweight models for continuous scanning with deep reasoning models that cross-validate findings and eliminate false positives — organizations can spot and remediate toxic combinations of permissions and configurations that look harmless in isolation, before an external attacker can ever piece them together.

Countering asymmetric threat speed and the new extortion playbook

Not every adversary will embrace machine speed the same way in 2027. While state-sponsored espionage actors often avoid loud, hyper-fast automated scripts that risk triggering alarms and blowing their long-term stealth, cybercriminals tend to go all-in on autonomous velocity.

What we're seeing right now is a variety of adversaries that are exploiting vulnerabilities before the vendors even know about it. Charles Carmakal
CTO, Mandiant Consulting

As endpoint defenses get better at blocking traditional file-encrypting ransomware, criminal crews are shifting to rapid, high-volume data theft and extortion — specifically targeting proprietary model weights, custom agent skills, prompt libraries, and cloud GPU compute (“LLMJacking”).

Preparation here means eliminating security silos. It’s a critical failure point to treat models, agent permissions, software supply chains, and cloud infrastructure as separate domains.

Instead, we want to unite them under a single, intelligence-led governance framework that allows defenders to lock down high-value autonomous assets and shut down extortion paths before data can leave the environment.

Red-teaming the agentic enterprise before adversaries attack

In the rush to capture productivity gains in the year ahead, many enterprises are deploying custom agents directly into core business workflows without testing how those systems behave under adversarial pressure. That mistake leaves guardrails vulnerable to bypasses, scanner blinding, and indirect prompt injection.

One of the things that the team's been doing over the past few years is doing a lot of red team exercises… with those agentic solutions, so that we have the opportunity to identify and fix the issues before an adversary has the ability to do that against victim organizations. Charles Carmakal
CTO, Mandiant Consulting

Continuous, proactive adversarial red-teaming must be a mandatory prerequisite before any autonomous workflow touches production. Systematically attempting to break into agentic systems, bypass safety controls, and manipulate model behavior prior to deployment is crucial to securing the agentic era.

Critically, model safety engineering should combine the results of those tests with frontline threat intelligence on novel attack prompts and evasion techniques. Defenders can then harden classifiers and neutralize new attack techniques before adversaries can use them against real victims.

Orchestrating specialized agent teams to prevent agent sprawl

Relying on a single generalist agent to defend an enterprise will fail in 2027 because deep technical skills do not transfer across domains. For example, an agent built to reverse-engineer malware is not the agent that should reason about an identity blast radius.

As departments across the business rapidly spin up uncoordinated, standalone agents without centralized oversight, agent sprawl is emerging as the next iteration of shadow IT, threatening to derail enterprise agent deployments through inadequate governance.

Technological adoption cannot outpace architectural governance… We have to prioritize machine speed defense. Security cannot remain a manual checkpoint that slows innovation down. Sandra Joyce
vice-president, Google Threat Intelligence

Just as a well-coordinated human team beats a lone generalist, the year ahead belongs to orchestrated networks of specialized hunter, decoder, and responder agents, each operating with its own domain depth, memory, and dedicated tools.

Succeeding at scale requires building the governance and orchestration layer first. The key here will be to establish graduated autonomy, shared state, clean handoffs between specialized agents, and a central coordinator that knows precisely when to act autonomously and when to escalate to a human.

To learn more about our cybersecurity guidance for the coming year, check out our Security 2027 video.

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Threat Intelligence news

The rise of LLMJacking: How threat actors monetize compromised resources: Mandiant responded to multiple incidents in 2026 in which threat actors hijacked enterprise access to large language models, a tactic known as LLMJacking that can generate large spikes in fraudulent cloud charges in a matter of hours. Read more.
Shutting the door on the XCPE botnet: Google and industry partners dismantled the XCPE backdoor that had assembled more than 1.3 million set-top boxes into a botnet. We assessed with high confidence that the backdoor and botnet were intentionally developed, deployed, and operated for financial gain by Videostrong Technology Company, an Android original equipment manufacturer and original design manufacturer. Read more.
ARTEX and friends: Enter agentic offensive frameworks: Since September, Google Threat Intelligence Group and Mandiant have observed multiple threat actors abusing the open-source, agentic pentesting frameworks ARTEX, Hexstrike, and CAIRN to conduct a broad range of actions across multiple phases of the attack life cycle. Read more.
Investigating ephemeral supply chain tampering: For the first time, Google Threat Intelligence Group (GTIG) and Mandiant have encountered a software supply chain compromise that was implemented with very short tampering windows that were active for only five to 10 minutes, and effectively evading traditional point-in-time triage and automated file scanning. Read more.

Please visit the Google Cloud blog for more threat intelligence stories published this month.

Introducing TPU Sync: Fast KV cache and weight transfer for inference and RL on TPU

Mon, 09 Nov 2026 13:00:00 +0000

As frontier AI evolves from stateless single-turn prompts to million-token agents, disaggregated serving, and continuous reinforcement learning (RL), the primary bottleneck in AI infrastructure has shifted from raw compute to orchestrating live model state — both the contextual state held in massive key-value (KV) caches during inference and the parameter state updated and broadcast from trainers to rollout samplers during RL. Whenever this state is trapped inside a single chip's memory or stalled on network transfers, AI accelerators sit idle.

We designed TPU Sync from first principles to serve as the model-state layer across TPU clusters — built on the architectural insight that accelerator High Bandwidth Memory (HBM), host DRAM, shared storage, and the data center network (DCN) should operate as a single, co-designed memory hierarchy. An open-source, native C++ library, TPU Sync moves TPU-tiled tensors directly across this hierarchy with asynchronous DMA transfers that overlap data movement with active computation.

For inference, it decouples TPU compute from network backpressure and turns host DRAM, local SSDs, and Google Cloud Managed Lustre into an automated, multi-tier KV cache; and for RL, it exposes low-level DMA and DCN primitives that higher-level RL frameworks can use to stream updated weights from trainers to samplers. By accessing device buffers in place rather than copying or locking them, TPU Sync consistently slashes KV transfer latency by over 90% and reduces Time to First Token (TTFT) by an average of 90% for heavyweight open-weight MoE models (235B–480B parameters) in prefix-caching and disaggregated-serving workloads, compared to running without TPU Sync.

Across this shared model-state layer, TPU Sync integrates directly into existing infrastructure stacks across PyTorch (via TorchTPU) and JAX — serving as the TPU KV cache connector for vLLM, SGLang, and llm-d disaggregated serving, while providing low-level transfer primitives for Pathways and multi-controller JAX (McJAX) RL pipelines.

Understanding TPU Sync: The kitchen analogy

To understand TPU Sync’s impact, imagine a high-end restaurant kitchen. Think of the TPU as an incredibly fast executive chef working on a tiny countertop (the chip's HBM). When preparing a massive feast, the chef needs lots of ingredients (the KV cache), which quickly clutter the counter.

The standard kitchen (without TPU Sync): When the small counter fills up, the chef has to toss out prepped ingredients and chop them again from scratch the next time an order needs them — stalling the kitchen on repetitive prep work (prefill recomputation).
The upgraded kitchen (with TPU Sync): TPU Sync adds a spacious side pantry (pinned host RAM), a shared walk-in cold storage room accessible to every station in the kitchen (shared cluster storage), and a coordinated team of runners. The runners slide full prep trays directly between the counter, the pantry, and peer stations without repacking them (direct DMA in native TPU layout).

Crucially, the runners swap trays in and out while the chef keeps cooking (asynchronous, non-blocking transfers).

Key use cases for TPU Sync

TPU Sync improves system throughput and latency in workloads requiring deep context, high concurrency, multi-turn reasoning and RL rollouts:

Long-context AI agents and multi-turn chats: When interacting with an AI agent over an extended 256K to 1M+ token session or analyzing massive documents, the model generates a continuously growing KV cache. Instead of allowing this state to exhaust HBM capacity, TPU Sync automatically tiers older KV blocks out of TPU HBM and into large pools of host RAM (with experimental tiering to shared secondary storage).
Enterprise applications with heavy system prompts (prefix caching): AI serving platforms receive thousands of queries that share common instructions or documents. Rather than recomputing prefixes that the model has already processed, TPU Sync extends the prefix cache into distributed host memory so concurrent sessions can reuse precomputed KV blocks and bypass redundant prefill computation.
Disaggregated serving architecture (prefill/decode): In modern enterprise deployments, workloads are isolated: Compute-bound nodes process the initial massive prompt (prefill), while memory-bound nodes draft the response token-by-token (decode). TPU Sync bridges this hardware gap by acting as a high-bandwidth Host-to-Host (H2H) streaming pipeline. It routes the processed context directly from the prefill nodes to the decode nodes, consistently delivering an over 90% reduction in data transfer overhead.
Reinforcement learning pipelines (experimental): Validated on internal RL workloads and included in the repository as an experimental module, TPU Sync provides low-level DMA and DCN transfer primitives that higher-level RL frameworks can use to synchronize model weights between trainers and inference samplers — including automatic resharding across different trainer and sampler parallelism layouts — to reduce the time rollout workers spend waiting on weight updates.

Breaking through the memory wall with TPU Sync

At its core, TPU Sync establishes a high-performance data transfer path between TPU HBM and host RAM, as well as across TPU hosts via the DCN. It achieves this through four key architectural advantages:

Layout-preserving direct DMA with in-place buffer aliasing: TPU Sync accesses the underlying JAX PJRT buffers or PyTorch tensors in place and moves slices directly between TPU HBM and page-locked (DmaMap-registered) host memory in a single hardware DMA. By keeping tensors in their native TPU physical tile layout across HBM, host DRAM, storage, and the wire, data lands ready for immediate Matrix Multiply Unit (MXU) execution with no Python serialization, no layout conversion, and no user-space bounce buffers.
Compute-overlapped transfers: Rather than taking an exclusive lock or making an on-device copy of static KV cache and weight buffers, TPU Sync creates raw PJRT buffer aliases (PJRT_RawBuffer) without holding PJRT usage locks so block-level transfers can run concurrently with active in-place computation, with built-in DMA pacing to keep large transfers from delaying kernel launches.
No XLA compilation in the data path: Because TPU Sync executes transfers and tile-aware strided resharding in native C++ outside the XLA graph, moving KV blocks or resharding weights across different trainer and sampler topologies does not trigger runtime compilation — keeping the data path fast and framework-agnostic.
High-bandwidth PCIe DMA and DCN streaming: Using the raw_transfer library’s direct PJRT raw-buffer DMA pipeline, TPU Sync reaches up to 223 GB/s of aggregate D2H and H2D throughput per Ironwood (TPU v7x) host out of the 256 GB/s PCIe Gen5 hardware ceiling, and streams across the DCN at up to ~192 Gbps on a single 200 Gbps NIC (or ~350 Gbps across dual 200 Gbps NICs with NUMA-aware multi-NIC striping).

TPU Sync exposes a low-level data transfer API, making it easier for developers to integrate high-speed TPU data transfer into higher-level frameworks.

Higher-level architecture

Built on top of its core transport and resharding primitives, TPU Sync provides a modular orchestration stack for distributed cache management:

Global KV store: A global registry that maps prompt prefix hashes to physical cache blocks, handles dynamic lookups, and coordinates LRU eviction and tier demotion.
TPU Sync controller: The orchestration layer that generates transfer plans, coordinates data movement across nodes, and handles lease management and recovery.
KV cache manager: The local execution engine that tracks block states, manages HBM, host DRAM (including /dev/shm warm-boot persistence) alongside experimental POSIX storage backends, and executes asynchronous DMA transfers.

Figure 1. TPU Sync operates as the model-state layer on TPU, unifying TPU HBM (Tier 1), pinned host DRAM (Tier 2), shared storage (Tier 3), and multi-host DCN under a single layout-preserving DMA data plane.

Solving disaggregated serving and prefix caching

Let us take a closer look at how TPU Sync helps with some of the use cases we listed in the earlier section. By treating host pinned memory as a substantial, high-speed Tier 2 cache, TPU Sync reduces the fragmentation and memory limits of standard HBM setups.

Prefill/Decode (P/D) disaggregated serving

In state-of-the-art serving topologies, prefill (compute-bound) and decode (memory-bound) phases are isolated on separate node pools. To prevent latency bubbles, TPU Sync bridges this gap with a highly optimized, three-step pipeline:

Direct-DMA offloading: Once a prefill node processes an initial prompt, TPU Sync immediately moves the resulting KV blocks into pinned host RAM via its asynchronous D2H DMA path.
Host-to-host streaming: The TPU Sync controller then orchestrates a high-bandwidth Host-to-Host (H2H) transfer, streaming the context directly to the assigned decode node.
Overlapped ingestion: As blocks arrive in the decode node's host RAM, TPU Sync triggers an asynchronous H2D transfer directly into TPU HBM. This happens concurrently with active decoding computation, substantially minimizing TTFT.

Host-powered prefix caching

For highly concurrent systems sharing large system prompts or running multi-turn agentic loops, TPU HBM is not large enough to hold multiple active cache blocks. TPU Sync extends the prefix cache out of HBM (Tier 1) into substantial, distributed host RAM pools (Tier 2). Because TPU Sync transfers are asynchronous, it overlaps the PCIe DMA loading of the next required prefix from host RAM with the compute of the current request — minimizing TPU idle time between requests.

For workloads that exceed even host DRAM capacity, TPU Sync also includes an experimental POSIX storage backend (Tier 3+) that asynchronously spills older KV blocks to local SSDs or shared Google Cloud Managed Lustre storage — pairing with llm-d prefix-affinity routing on GKE so replicas can recall cached prefixes across tiers without recomputing prompts.

Sample performance results

P/D disaggregated inference result highlights:

Over 90% KV transfer overhead cut: For heavyweight models like Qwen3 235B, median KV transfer latency dropped from several seconds down to a flat ~230 ms.
Substantial TTFT reduction: Demonstrated an average of 60% reduction in TTFT for heavyweight models (Qwen3 235B) under controlled, low-concurrency testing benchmarks, and an average of 90% for 480B under low concurrency.
Substantial high-traffic scalability: During sustained traffic spikes, TPU Sync achieves an average of 30% to 70% increase in token output throughput for 235B, depending on traffic distribution.

Prefix caching result highlights:

Expands KV cache capacity beyond HBM: When heavyweight MoE model weights consume most of device HBM, on-chip memory can hold only a few long-context prefixes. Offloading into pinned host DRAM expands effective cache capacity by an order of magnitude.
Sustains high cache-hit rates under concurrency: Pooling distributed host RAM keeps shared system prompts and multi-turn agent contexts warm across thousands of concurrent sessions instead of thrashing HBM.
Significantly reduces prefill recompute: Directly bypasses heavy compute layers for repeated instruction blocks and system prompts, reducing average TTFT by up to 90% on 64K-context workloads while delivering over 50% higher throughput and a 30% reduction in P99 inter-token latency (ITL).

Please see the note¹ below on the testing configuration.

Ecosystem adoption and partnerships

TPU Sync serves as the open, composable memory substrate across our PyTorch-on-TPU and Kubernetes serving ecosystem, built in close collaboration with key open-source maintainers and infrastructure partners:

Inferact (vLLM-TorchTPU): Co-engineered with Inferact, maintainers of vLLM-TorchTPU, TPU Sync is integrated directly via vLLM's native KVConnector interface. It powers Direct-DMA host DRAM offloading and multi-host P/D disaggregation across frontier open-source models.
RadixArk (SGLang-TorchTPU): In partnership with RadixArk, maintainers of SGLang-TorchTPU, TPU Sync serves as both the high-speed P/D disaggregation transfer backend and the native I/O backend for SGLang’s hierarchical cache.
llm-d 1.0 integration: llm-d 1.0 pairs with TPU Sync on GKE to deliver global prefix-cache-affinity routing, per-rank decode session pinning, and experimental two-stage capacity spill to Google Cloud Managed Lustre.
Validated by frontier AI labs and enterprises: Frontier AI organizations are already adopting our PyTorch-on-TPU serving stack and evaluating TPU Sync multi-tier architectures for production deployments — including Meta, alongside major enterprises like Wayfair, and multi-silicon partners.

Get started

TPU Sync is available today as open source with Python wheels for both PyTorch (tpu-sync-torch) and JAX (tpu-sync-jax), and is integrated with vLLM-TorchTPU, SGLang-TorchTPU, and llm-d on GKE. Visit the GitHub repository to explore the architecture docs, C++ and Python APIs, and deployment guides.

We are committed to working with the open-source community to advance the model-state layer on TPU — supporting new frontier architectures, expanded storage tiering, and longer-horizon agentic and RL workloads. For more up-to-date performance results, please see our ongoing efforts on public benchmarks (InferenceX).

^{1. Performance results are based on internal testing conducted in July 2026 on TPU v7x (2x2x1 topology), configured with PCIe Gen5 (x16) per TPU chip and one or two 200 Gbps NICs enabled per host, running tpu-inference v0.24.0 (using JAX 0.10.2 and libtpu 0.0.42.1). Metrics presented represent average and median performance improvements observed under controlled laboratory conditions. Actual real-world performance, cache hit rates, and latency reductions will vary based on system configuration, network bandwidth, concurrent traffic, and specific user workload patterns.}

Our newest TPUs: 8tx and 8i Max, built for the agentic era

Mon, 09 Nov 2026 13:00:00 +0000

Editor’s note: This blog was originally published on News from Google.

We first designed TPUs more than a decade ago motivated by Google’s internal demands, including voice recognition and language translation. Eight generations of TPUs later, the use cases have exploded, covering: i) training and serving our own Gemini models, ii) agentic workloads, iii) delivering services to Google Cloud customers on our cloud infrastructure, and most recently, iv) allowing customers to deploy TPUs directly into their own data centers. TPUs are now a rapidly growing business with nearly 100 customers — including frontier labs, trading firms, retailers, universities, and more.

At Google Cloud Next in April, we introduced two new specialized TPUs in the same generation — 8t for large-scale training and 8i for real-time reasoning. TPU 8t and 8i offer up to 2.7 times better training performance and 80% better inference price performance, respectively, versus our previous generation of TPUs.

Today, we’re taking that a step further, expanding our eighth-generation TPU portfolio with two additional systems: TPU 8i Max for massive context windows and the highest compute density per rack, and TPU 8tx for secure and confidential inference. This is the first time we have introduced four TPU systems in a single year.

Our expanded eighth-generation TPU portfolio: TPU 8t, TPU 8i, TPU 8tx, and TPU 8i Max.

While we’ve always co-designed our TPUs in partnership with Google DeepMind, we’re now increasingly getting feedback from external organizations on their specific needs. With computing moving from human to agent scale, businesses are asking for accelerators that can reason across AI models’ massive context windows. In addition, some highly regulated customers are asking for specialized encryption capabilities for unique workloads.

The new TPU systems we are announcing today address this new reality. Coupled with our recent PyTorch announcements, our aim is to make TPUs more easily accessible to every developer and Cloud customer. By combining modular custom silicon with an open and easy-to-use software stack, we can deliver new hardware and system configurations at the pace of the AI industry.

Building the computer around the chip

Today, we’re announcing two new TPU systems — and there’s an important difference between a new chip and a new system.

Designing a new chip means laying out billions of microscopic transistors on a silicon wafer in a multi-year process bound by the laws of physics and a complex, long-duration manufacturing lifecycle. A system, on the other hand, is about how we bring together the compute, networking, and storage around the chip. This can include how they are packaged with the chips, to quickly meet emerging customer needs and research use cases.

When we built our very first TPU more than a decade ago, we knew AI software would evolve much faster than silicon manufacturing ever could. So we made a foundational bet on system balance and modularity, architecting our TPUs as building blocks as part of our AI Hypercomputer, so engineers could rapidly adjust packaging, high-speed memory, optical networking, and security around them. That approach allows us to deliver specialized supercomputers in a matter of months rather than years.

Specializing for secure, high-context AI

Each system in our eighth-generation TPU portfolio targets a distinct part of the AI lifecycle. TPU 8t and TPU 8i established the baseline for this generation of chips:

TPU 8t was designed to be a training powerhouse for massive foundation models with maximum “goodput” — or the useful intelligence delivered per watt of energy used.
TPU 8i is the reasoning engine, designed to break the memory wall, cutting inter-chip communication delays by up to half.

Now, as enterprises bring autonomous agents into production, we have created two new configurations of these foundational systems to solve two urgent new challenges: hardware-level data security and million-token context scale.

TPU 8tx

TPU 8tx, our third eighth-generation system, is optimized for secure and confidential inference and is also great for small-scale training. It’s built for regulated enterprises and governments running frontier or proprietary models and autonomous agents alongside their most sensitive data.

Normally, data is encrypted when stored or sent over a network, but has to be unlocked in memory while a computer processes it. Built on the same silicon as TPU 8t and paired with 5th Generation AMD EPYC CPU hosts, TPU 8tx builds security directly into the hardware layer, keeping sensitive data and model weights protected and isolated even while in active use. And by adopting the same low-latency Boardfly network we created for real-time reasoning, TPU 8tx keeps multi-step agents moving, delivering up to 2x lower latency compared to TPU 8t with a near-zero performance penalty.

Pairing training-grade compute with a fast inference network also means organizations can simultaneously run and customize their models on a single system. TPU 8tx enables teams to fine-tune models using their private data, distill large models into smaller ones, and train their agents through reinforcement learning, all without needing to migrate sensitive data between separate clusters.

Designed for sensitive enterprise workloads, TPU 8tx provides hardware-level confidential compute for private fine-tuning and secure agent execution.

TPU 8i Max

Our fourth system, TPU 8i Max, is built for a different problem: the memory wall.

When dozens of AI agents work together across an entire software codebase or hours of video at once, memory bandwidth quickly becomes the bottleneck. So we took two low-latency TPU 8i chips and combined them onto a single package.

TPU 8i Max scales compute and memory in lockstep, delivering double the compute, double the SRAM and double the HBM bandwidth of TPU 8i, so processors remain equipped with essential data and so that large workloads do not stall. Further, because TPU 8i Max fits twice the silicon within the existing rack footprint, data centers can deploy significantly more computing power per square foot and per watt.

The dual-chip TPU 8i Max system, engineered for massive context windows and maximum compute density.

While flexible across workloads, together the four systems in our eighth-generation TPUs give customers a purpose-built accelerator for every stage of the agentic lifecycle. TPU 8t for scale-up training, TPU 8i for low-latency reasoning, TPU 8tx for confidential computing, and TPU 8i Max for massive context windows and the highest compute density per rack.

Opening up TPUs to every developer

When we launched the first TPU more than a decade ago, there were only a handful of companies running workloads that needed the processing power of a TPU. The use cases were specialized, so engineers would use optimized frameworks and hand-tuned code.

Over the years, as AI has advanced, more and more companies began using accelerators. Now, AI adoption is at a point where TPUs are excellent for a wide range of companies and use cases, and as a result, we’ve been working systematically to remove barriers to adoption so that TPUs are as easy-to-use as general-purpose hardware.

This investment comes together today across these key areas:

Opening up TPU development to every PyTorch developer; no code rewrites needed: Last month, we open-sourced TorchTPU, so PyTorch code and Hugging Face models now run natively on TPUs, alongside out-of-the-box TPU backends for leading open-source inference engines like vLLM and SGLang. And to make hardware optimization effortless, our new open-source TPU agent suite (now in preview) plugs directly into coding tools like Claude Code, Cursor, and Google Antigravity to automate TPU onboarding in minutes, diagnose bottlenecks, and autotune custom kernels using plain language. For production deployments, our new open-source XLA Serving Engine lets you train models in Python and serve them directly in a low-latency C++ runtime across TPUs, GPUs, and CPUs.
Industry-leading production performance on InferenceX and AgentX benchmarks: In production, what really matters is outcomes on real AI workloads rather than theoretical peak chip speeds. On industry-standard InferenceX and AgentX benchmarks, which test realistic agentic coding and long-context work, our native vLLM-TorchTPU software stack on TPUs achieved state-of-the-art price-performance marks. New evaluations demonstrate these advantages extend across a wide range of frontier open models and open-source serving stacks while maintaining high model accuracy. Upcoming software optimizations on this same hardware will drive those results even further.
Eliminating idle compute waste for autonomous agents: Because AI agents are stateful, they frequently pause for seconds or hours while waiting on a database lookup, an external software tool, or human feedback. To make our agents more resource-efficient during those pauses, our new Agent Compute Engine software automatically suspends and resumes idle sessions, cutting operational infrastructure costs.
Breaking through the memory wall with the model-state layer (TPU Sync): Building the computer around the chip also means unifying memory, storage, and networking in software. As agents reason across million-token contexts and continuous reinforcement learning (RL) loops, memory quickly becomes the bottleneck. We’re open-sourcing TPU Sync, the model-state layer for TPU clusters. TPU Sync turns chip HBM, host DRAM, shared storage, and the data center network into a single, co-designed memory hierarchy — automatically tiering massive KV caches off the chip in milliseconds, streaming context across disaggregated prefill and decode nodes, and synchronizing RL weights to cut KV transfer latency and Time to First Token (TTFT) by more than 90%.
Scaling distributed workloads across TPUs from a single controller: Now generally available on Google Kubernetes Engine (GKE), Cloud Pathways brings the Google DeepMind runtime behind Gemini to every Google Cloud customer. Using a single-controller architecture and pathwaysutils, you can orchestrate large-scale TPU systems across multiple slices as if they were one local machine for pre-training, reinforcement learning, and multihost inference. Cloud Pathways keeps long-running jobs moving through hardware disruptions with colocated Python sidecars, sub-minute slice hot-swapping, and elastic resharding.
Stitching custom TPU topologies on-demand with GKE dynamic slicing: Now generally available, GKE dynamic slicing decouples physical hardware provisioning from logical slice formation using optical circuit switching (OCS) technology. By dynamically assembling TPU cubes into larger topologies (super-slicing) or partitioning them into smaller, isolated workloads (sub-slicing) on-the-fly, this update completely eliminates rigid, manual nodepool recreation cycles. This topology agility slashes job startup times by up to 5x and accelerates fault recovery by up to 4.5x, maximizing overall fleet utilization and workload goodput across all TPUs.

Co-designing with a decade of experience

Shipping four TPU systems and a unified, open software stack in a single year only happens when silicon architects, networking engineers, compiler teams, and Google DeepMind researchers build side-by-side.

Increasingly, we’re even tapping into AI to ideate and build the next generation of AI infrastructure. From using Google DeepMind’s AlphaChip reinforcement learning system to using Gemini to deliver chip layouts in hours rather than weeks, we’re using TPU-powered AI services to rapidly accelerate next-generation TPU hardware development. This is true co-design and a unique Google differentiator.

Learn more about our new TPU systems by tuning into today’s broadcast at 9 a.m. PT.

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