Kitesurf, our experimental browser engine for AI agents, is now open source. Inspect the code, run it locally, or deploy your own instance on Cloudflare Workers.
AI-orchestrated penetration testing platforms like ARTEX have become increasingly powerful tools enabling effective attacks on exposed infrastructure. Here we share Cloudflare’s observations and recommended countermeasures.
When an application slows down or memory usage keeps climbing, knowing which code is responsible can help you find a fix faster. Continuous profiling gives you that visibility, so you can see where your application spends resources such as CPU time and memory. We’ve made updates to Pyroscope , our open source continuous profiling database, that make it easier to profile your applications, find performance bottlenecks, and optimize your code, whether you're doing the work yourself or with an AI agent. Highlights include: Profile more of your workloads: Memory profiling for Python and Rust, and .NET support on Windows, helps you find performance bottlenecks across more of your services. Let AI agents work with your profiling data: Pyroscope commands in gcx , the Grafana CLI, let an agent query your profiles directly. See more of your profiling data in Grafana: Exemplars and a new Call Tree view help you explore your profiles visually, whether you're investigating on your own or looking at what an agent found. Many of these capabilities build directly on Pyroscope 2.0 , the ground-up rearchitecture of Pyroscope that makes continuous profiling more cost-effective at scale (in our production environment, it reduced the symbol storage footprint by up to 95%). For example, exemplars require the v2 storage layer that Pyroscope 2.0 is built on. Read on to learn more about some of the latest updates. And for a full list of recent changes, see the release notes for Pyroscope and Profiles Drilldown . Profile more of your workloads Memory profiling and Windows support have been some of the most frequently requested features for Pyroscope. If you run Python or Rust services, you can now use the Pyroscope SDKs to see where their memory goes, not just their CPU time. If you run .NET applications on Windows, you can now profile those, too. Find memory leaks and heavy allocation: memory profiling for Python Memory issues can be hard to track down from metrics alone. A container's memory graph shows that usage is climbing, but not which code is allocating that memory. CPU profiles don't answer that either, since they show where your code spends time. Memory profiles show which code paths allocate the most memory and which allocated the memory that's still in use, which is what you need when a service's memory keeps growing, or its allocations drive up garbage collection work. The Python SDK now supports memory profiling, so you can see which code allocates the most memory and which code allocated the memory that's still in use, alongside the CPU profiles you already collect. Enable it with mem_enabled=True: pyroscope.configure( application_name = "my.python.app", server_address = "http://my-pyroscope-server:4040", mem_enabled = True, ) To learn more, see the PR and the Python SDK docs . Find memory leaks and heap growth: memory profiling for Rust In addition to CPU profiling, the Pyroscope Rust SDK now supports memory profiling through the jemalloc allocator. It reports in-use memory, so you can see which code paths allocated the most live memory and track down heap growth and leaks. To learn more, see the PR , the Rust SDK docs , and the jemalloc example . Profile .NET applications on Windows If you run .NET applications on Windows, including .NET Framework applications hosted in IIS, you can now send profiles from them to Pyroscope and find hot spots without migrating them first. Windows support is for x64 , starting with version 1.3.0 of the profiler. On .NET 8, 9, and 10, you get the same profile types on Windows as on Linux, enabled with the same environment variables. On .NET Framework 4.7 and later, CPU and wall-time profiling are available. To learn more, see the PR and the .NET SDK docs . Note: this feature is currently in public preview. Let AI agents work with your profiling data Without access to your profiles, an AI coding agent can only reason about why a service is slow from the code in front of it, or you have to copy flame graphs and numbers into the conversation by hand. With access to your profiles, it can work from the same data you would, with visibility into which services use the most resources, which functions are responsible, and what a specific profile looked like at a specific time. gcx , the Grafana CLI, gives you and your AI coding agent structured access to your Grafana instance, from dashboards and alerts to metrics, logs, traces, and profiles. gcx enables agent mode automatically in many coding agents , such as Claude Code, GitHub Copilot, and Cursor, and you can enable it yourself for any other agent with --agent. gcx now also includes gcx profiles commands for querying Pyroscope. To see gcx in action with Pyroscope, both in the terminal and with an AI agent, watch this community call . Below are some of the ways you can use gcx with your profiling data, whether you run the commands yourself or an agent runs them for you. You don't need to run your own stack to try them. Grafana Play has demo data you can query, so you can point gcx at it by adding a play context: gcx config set stacks.play.grafana.server https://play.grafana.org gcx config set stacks.play.grafana.org-id 1 gcx config set contexts.play.stack play export GCX_CONTEXT=play GCX_CONTEXT selects the play context for commands in this terminal session, without changing your default context. The commands below also use the Play profiles data source, grafanacloud-profiles . To use your own data, open a new terminal, leave out -d grafanacloud-profiles , and change the service name. Start with the services using the most resources When you run many services, it's not always clear which one to look at first. gcx profiles metrics --top ranks your services by a profile type, such as CPU, over a time window, so you or your agent can start with the ones using the most resources: gcx profiles metrics '{}' \ -d grafanacloud-profiles \ --profile-type process_cpu:cpu:nanoseconds:cpu:nanoseconds \ --since 1h \ --top To try this with your AI coding agent and the Grafana Play data, use this prompt: Using gcx with the play context, find which services used the most CPU over the last hour in the grafanacloud-profiles datasource. To learn more, see the PR and the gcx profiles metrics CLI reference docs . Go from the combined view to the exact profile with exemplars An aggregated profile blends every instance of a service together over the time range, so a single misbehaving process can be hard to spot. Exemplars show individual profiles ranked by their value for the profile type you query, such as CPU time or memory, so you or your agent can go straight to the most expensive ones and see what happened in each, instead of only the combined total. gcx profiles exemplars lists the most expensive individual profiles, and you can pass one of their profile IDs to gcx profiles query to see the top functions in just that profile: gcx profiles exemplars profile '{service_name="checkoutservice"}' \ -d grafanacloud-profiles \ --profile-type process_cpu:cpu:nanoseconds:cpu:nanoseconds \ --since 1h gcx profiles query '{service_name="checkoutservice"}' \ -d grafanacloud-profiles \ --profile-type process_cpu:cpu:nanoseconds:cpu:nanoseconds \ --since 1h \ --profile-id If your profiles are linked to traces, you can also narrow a query to a single span or trace to see the code that ran during a slow operation or request. To try this with your AI coding agent and the Grafana Play data, use this prompt: Use exemplars to find the most expensive CPU profile for checkoutservice in the grafanacloud-profiles datasource in the last hour, and summarize its most CPU-intensive functions. To learn more, see the PRs for exemplars , profile IDs , and span and trace selectors . Also see the exemplars CLI reference docs and query CLI reference docs . Export profiles to use with other tools Sometimes you want the raw profile: to open it in tools you already use, to attach it to an issue, or to keep a snapshot from before a change so you can compare later. Add -o pprof to gcx profiles query to download the profile as a pprof file, which you can open with go tool pprof . You can also start from the Grafana UI. With the release of Profiles Drilldown 3.0 in October, you can click the Export profile data download icon above the flame graph and select gcx command , which copies a gcx profiles query command that downloads the profile you're looking at as a pprof file. In the diff flame graph view, gcx commands copies two commands, one for the baseline profile and one for the comparison. To learn more, see the PRs for -o pprof and copying gcx commands , the Export profile data in the Profiles Drilldown docs, and the gcx profiles query CLI reference docs . See more of your profiling data in Grafana The Grafana UI now includes exemplars to look at one profile at a time, and the Call Tree , to follow exactly how your code reached an expensive function. Use them on your own, or to look at what an agent found and see the same data for yourself. See one profile at a time with exemplars The same individual profiles that gcx profiles exemplars returns are also in the Grafana UI. When you're looking at a spike, exemplars let you see the individual profiles from single processes at that moment, instead of only the combined total for the whole time range. Exemplars show up as markers on the time series, plotted at each profile's value. In Profiles Drilldown , they're on by default in the Flame graph and Labels views, and you can turn them on in other views. Click a marker to see its labels, such as the host or, if you run on Kubernetes, the pod and namespace. Then select View profile to see the flame graph for just the profile. In Explore, turn on Exemplars under the Pyroscope query's Options . To learn more, see our docs on exemplars concepts , drilling down to an individual profile , and available views , as well as this community call . Follow call paths with the Call Tree view Flame graphs and the top table are good at showing where resources are spent, but in deep or wide stacks, it can be hard to follow the exact path your code took to reach an expensive function. The new Call Tree view shows the same data as an expandable tree, so you can follow that path one call at a time. It's available in Profiles Drilldown 2.3.0 and later, in Explore, and in the flame graph panel starting in Grafana 13.3 . On earlier Grafana versions, enable the flameGraphWithCallTree feature toggle . When an expensive function, such as a serialization or logging call, is called from many places, use Show callers to see every path that leads to it and which caller matters most. You can also show the Call Tree side by side with a flame graph, and both stay in sync as you focus on a function. To learn more, see the PR , the call tree mode docs, and the August community call . How to learn more To see the full list of recent improvements and fixes, please refer to the release notes for Pyroscope and Profiles Drilldown , and the gcx repository . If you are interested in hearing more about Pyroscope news, please join us on the Grafana Labs Community Slack or start a conversation in GitHub Discussions . We'd love to hear what you think.
As enterprises move AI agents from experimental prototypes into production, data and platform teams are discovering that deploying an agent is only half the battle — you also need to see how it reasons, which data it touches, and what it costs to run. Today, we are announcing expanded AI agent observability and evaluation capabilities across Google Data Cloud, giving you unified visibility into your first-party (1P) data agents in BigQuery and Looker as well as custom third-party (3P) agents built with agent development frameworks such as Agent Development Kit (ADK) or LangGraph. Putting conversational and autonomous agents into production requires solving three operational challenges: tracing multi-step reasoning while protecting sensitive data, controlling token consumption and query spend, and evaluating answer accuracy before prompt or schema changes reach users. Let’s explore how our new agent observability and evaluation capabilities help you do this. What's new: Agent observability across Data Cloud Running agents at scale means you need to see how they perform, what they cost, and what actions they take. We're rolling out a unified telemetry framework across Google's Agentic Data Cloud that works the same way for every agent: Capture telemetry from the agent, land it in BigQuery tables you own, and analyze it wherever your teams already work. Unified agent observability framework capturing logging telemetry across 1P and 3P agents into BigQuery to surface performance and usage insights across developer and admin tools. 1. Capture telemetry from every first-party and custom agent First-party data agents: Out-of-the-box Conversational Analytics agents emit telemetry automatically with no setup required. Queries routed to BigQuery data agents in Gemini Enterprise through agent-to-agent (A2A) workflows (now GA) send Cloud Monitoring metrics, Cloud Trace spans, and BigQuery conversation logs straight into your agent's project. Custom agents: Agents built with ADK or LangGraph stream the same structured telemetry into BigQuery through single-line SDK instrumentation. 2. Land telemetry in one governed home in BigQuery Conversation logging in BigQuery (GA): Managed 1P conversation logging writes to BigQuery tables you own, so you can inspect Conversational Analytics traces, including prompts and responses, directly in your data warehouse. Governed by default: The Conversational Analytics API , now GA, includes always-on admin audit logs and optional data-read audit logs in Cloud Audit Logs , and supports VPC Service Controls, customer-managed encryption keys (CMEK), and regional and multi-regional data residency . 3. Analyze with dashboards wherever your teams work In BigQuery (GA): Agent monitoring provides prebuilt dashboards in BigQuery Studio to track usage, active sessions, top knowledge sources, and latency across BigQuery Conversational Analytics agents. BigQuery Conversational Analytics dashboard in BigQuery Studio, giving admins visibility into usage, active sessions, top knowledge sources, and latency. In Looker (GA): System Activity dashboards for Conversational Analytics, now GA in Looker (Google Cloud core) and Looker (original), track engagement and token usage, while user ratings and query review in the Responses & feedback tab are in preview. Looker Conversational Analytics dashboard in System Activity, giving admins visibility into usage, token consumption, and user feedback. Build your own: Create interactive reports with the open-source Data Studio template or deploy the ready-made Looker Block for Agent Analytics for real-time custom agent monitoring. Custom Data Studio dashboard built on BigQuery Agent Analytics logs to track token consumption, latency, and user engagement. Looker Block dashboard built on BigQuery Agent Analytics telemetry to monitor session volumes, tool execution latency, and token cost distribution. Tracing agent reasoning and protecting privacy Enterprise observability requires balancing diagnostic telemetry with strict data privacy boundaries. Because tracking features involve ingestion and storage costs, Google Data Cloud separates operational telemetry from conversation content so admins and agent owners can govern each layer independently. At the project or organization level, admins can enable Cloud Monitoring (aggregate metrics such as request volume, latency, and token usage) and Cloud Trace (step-by-step spans for each request) across their environment, using Cloud Trace observability scopes to consolidate telemetry across multiple agent projects. To protect sensitive data, these operational metrics and sanitized first-party trace spans exclude customer core content (such as user prompts, model responses, internal thoughts, and generated SQL). When you need deeper transcript auditing, agent owners and editors can opt in to conversation logging on a per-agent basis: Governed conversation logging in BigQuery: When enabled, a Google-managed service agent streams user prompts, responses, and turn-level telemetry into a customer-owned BigQuery table ( agent_events ) via the BigQuery Storage Write API, inheriting your dataset's IAM permissions, CMEK, and VPC Service Controls. Built-in privacy guardrails enforce a fail-closed check before each turn — skipping logging whenever a knowledge source uses row-level security, column-level policy tags, or dynamic data masking — while fail-open execution keeps your users' chat sessions uninterrupted if a logging error occurs. Full trace trees for custom 3P agents: While 1P conversation logging intentionally strips low-level tool arguments and internal SQL, developers building custom agents on ADK or LangGraph can instrument their code with the open-source ADK plugin ( BigQueryAgentAnalyticsPlugin ). With a single-line setup, the SDK streams full tool-invocation execution trees, time-series telemetry, and multimodal payloads straight into BigQuery. In-warehouse root cause and drift analysis: Because conversation logs land natively in your agent_events table, you don't need separate export pipelines to diagnose failures. You can run BigQuery AI functions (such as AI.GENERATE to summarize error patterns and AI.EMBED with VECTOR_SEARCH to cluster recurring issues) or use the open-source bigquery-agent-analytics Python SDK and Colab starter notebook for post-hoc semantic drift clustering and LLM-as-a-judge sentiment analysis. Controlling token costs with FinOps guardrails As agent adoption grows, FinOps and platform teams need clear visibility into consumption alongside active controls to prevent runaway spend. With promotional trial pricing currently available for Conversational Analytics, now is a good time to establish baseline usage and cost guardrails: Granular token and latency tracking: In BigQuery conversation logs, each turn records input, output, thought, cached, and total token counts alongside total turn latency, while Looker System Activity reports estimated input and output tokens across your business intelligence (BI) workloads. Turnkey observability dashboards: You can track projected token usage and engagement in the BigQuery Studio Monitoring tab and native Looker System Activity dashboards, or query agent_events log data via BigQuery SQL using the open-source seven-page Data Studio report template and self-hosted Streamlit app in the BigQuery Agent Analytics GitHub repository . Active query and token spend caps: To prevent unexpected charges before they happen, you can configure per-query BigQuery byte scan caps ( big_query_max_billed_bytes ) on your agent, which use dry-run estimates to block oversized queries without incurring on-demand scan charges. For BigQuery and the Conversational Analytics API, you can also enforce daily input and output token quotas (in preview) per project, user, or agent in the Google Cloud console and gcloud CLI. Evaluating and benchmarking your AI agents Runtime observability works alongside systematic evaluation. While logs and traces tell you how an agent behaved in production, evaluation benchmarks verify whether updates to your prompts, models, or data schemas improve SQL accuracy and answer quality before users experience a regression: Benchmarking with Prism: Our open-source evaluation tool, Prism , lets you test Conversational Analytics agents grounded in BigQuery tables or Looker Explores via the Conversational Analytics API using reusable test suites, regex assertions, and multi-region agent discovery (deployed behind an access layer such as Cloud IAP). Unified evaluation and production analytics: Prism can stream completed evaluation runs directly into day-partitioned BigQuery tables in your project, placing your pre-deployment benchmark scores right alongside your production agent_events logs for side-by-side analysis in BigQuery and Looker. Native BigQuery evaluations: First-party agent evaluations in BigQuery (built on the Gemini Enterprise Agent Platform evaluation service with bring-your-own-service-account support) are available in preview via API and a guided Colab notebook. Getting started Ready to bring visibility, governance, and cost control to your AI agents? Here is how to get started in three steps: Enable first-party agent observability. Turn on Cloud Monitoring and Cloud Trace in your project and opt in to per-agent conversation logging using the BigQuery agent monitoring guide , and track BI token consumption with the Looker Conversational Analytics overview and Looker System Activity documentation . Instrument custom agents and deploy analytics dashboards. Add the BigQuery Agent Analytics SDK to your ADK or LangGraph agents, launch the open-source Data Studio report template or Streamlit app, and explore in-warehouse log analysis with the Colab starter notebook . Benchmark your agents and set cost guardrails. Run automated accuracy suites with Prism , and configure BigQuery byte scan caps and daily token quotas via Manage BigQuery costs for agents .
Computational scientists and HPC engineers have always been among the earliest adopters of new computing paradigms, and agents are no exception. Today, research and engineering teams use AI models and agents for things such as ingesting dense scientific literature, optimizing code, and autonomously steering simulations. Yet moving from an agent that suggests code optimizations to an agentic system that reliably architects, provisions, and optimizes production HPC workloads at scale takes more than a general-purpose reasoning model. Without deep infrastructure and domain context, an unguided agent can still pair incompatible storage tiers with compute-optimized VMs, overlook placement policies, or stall when diagnosing a multi-node MPI fabric error. That challenge led our team to ask a simple question: What if we could package our deep HPC and scientific expertise into reusable agent skills, turning complex setup and optimization into guided workflows? At SC26, we are showing how Google Cloud brings agentic HPC to life with reusable skills that teach agents how to build and run HPC workloads. In addition, we’re also demonstrating evolutionary agents that optimize your algorithms at cloud scale, and purpose-built supercomputing infrastructure designed for where HPC meets AI. Teaching agents to speak HPC: From blueprints to skills Organizations rely on Cluster Toolkit to deploy repeatable, production-ready HPC environments on Google Cloud through declarative YAML blueprints. Now we are expanding these capabilities by pairing Cluster Toolkit with composable agent skills with self-describing, deterministic tools, validated through automated, end-to-end testing and available for download from the Google Skills and DeepMind Science Skills repositories. Together, this delivers: Infrastructure skills that validate before they deploy : When you ask an agent to stand up a cluster, the skill directs it to first audit your compute and storage quotas, run rapid canary checks for regional capacity, match your workload to a validated Cluster Toolkit blueprint, and wait for human approval, all before running the gcluster deploy command. Workload-specific skills that know the science : After your cluster is live, domain skills connected to Google Skills and DeepMind Science Skills guide the agent through things like configuring OpenFOAM and Siemens STAR-CCM+ fluid dynamics runs, tuning GROMACS and AlphaFold 3 life-sciences pipelines, or launching Devito and Open Porous Media (OPM) subsurface simulations with browser-based 3D visualization. Built-in governance and Day-2 diagnostics : Agents operate inside strict IAM, Kueue quota, and budget guardrails, with full provenance in Cloud Logging and structured troubleshooting skills ( gke-kueue-debugging ) to resolve stuck queues or node drains automatically. Scaling from local prototype to breakthrough code Configuring and running existing solvers is only part of the discovery loop. Across financial services, electronic design automation (EDA), manufacturing, logistics, pharmaceuticals, and AI training, competitive advantage comes from having the fastest underlying code and algorithms. Historically, squeezing out performance gains required scarce optimization specialists and costly brute-force compute. Developed by Google DeepMind, AlphaEvolve changes the equation by using a Gemini-powered evolutionary loop to iteratively mutate, benchmark, and optimize code and 3D engineering parameters against target metrics. The AlphaEvolve on Cloud Batch solution, streamlines the process of moving from a seed algorithm on a laptop to massive parallel experimentation. With Batch, you get: Automated parallel evaluation : A single gcluster blueprint provisions the AlphaEvolve controller environment and uses Batch to fan out hundreds of candidate evaluations concurrently across single-VM sandboxes or multi-node MPI task groups. Interactive control from your preferred surface : Researchers can steer evolutionary experiments and inspect candidate lineage through hosted Python notebooks, or integrate optimization runs directly into their existing CLI workloads and agent-driven development tools. Evaluation at production scale : Evolving an algorithm on a single node with slow networking risks producing code that only performs well at small scale. By evaluating candidates across Google Cloud compute instances with Cloud RDMA and Google Cloud Managed Lustre — and using Spot VMs or Dynamic Workload Scheduler (DWS) to balance cost against turnaround time — you discover algorithms tuned for real supercomputing fabrics, permanently lowering the compute cost of every future production run. Supercomputing infrastructure under the hood An autonomous workflow is only as capable as the infrastructure beneath it. As agentic pipelines pivot dynamically between memory-bandwidth-bound physics solvers, domain foundation models (like AlphaFold 3 , AlphaGenome , and WeatherNext ), and large-scale AI training, Google Cloud provides a unified hardware and orchestration fabric managed by Cluster Director that offers: Tightly coupled HPC and scale-out compute : Run classic MPI simulations on H4D instances powered by 5th Gen AMD EPYC processors and Cloud RDMA, including 3 TB high-memory shapes built for large EDA and engineering models. Alternatively, you can maximize performance-per-watt on Arm-based C4A instances powered by Google Axion. Next-generation GPU acceleration : Run coupled physics-AI pipelines and multi-node GPU simulations on A4 and A4X instances (NVIDIA Blackwell B200 and GB200 NVL72), scale frontier AI training and reasoning on newly available A5X bare metal instances built on the NVIDIA Vera Rubin NVL72 platform, and use G4 instances with NVIDIA RTX PRO 6000 Blackwell GPUs to accelerate CAE, CFD, and 3D visualization while reducing per-core software licensing costs. 8th gen TPUs for the agentic era : Deploy 8th generation TPUs , featuring TPU 8t for large-scale pre-training, reinforcement learning, and accelerated mathematical modeling and simulation (ModSim), alongside TPU 8i for high-throughput, low-latency reasoning and agentic inference. Additionally, Batch now supports running jobs on single-host TPU slices , providing flexible provisioning options for accelerated HPC and AI/ML workloads. Sub-millisecond parallel storage : Reduce data bottlenecks between simulation and AI stages with Google Cloud Managed Lustre , delivering up to 10 TB/s of read throughput, GPUDirect Storage over RDMA, and an 80 PB Dynamic Tier with Cloud Storage Mirroring. For even more performance, you can pair it with Cloud Storage FUSE local SSD caching. Experience it live at SC26 in Chicago Agentic HPC is not about turning unguided models loose on a terminal. This shift is about combining the reasoning power of Gemini with the codified engineering of Cluster Toolkit skills, evolutionary optimization on Batch, and purpose-built supercomputing hardware — so your teams spend their time on discovery rather than infrastructure plumbing. Visiting SC26 (November 15–20)? Stop by Booth #3418 at McCormick Place West for live demos of Antigravity for HPC , the HPC Prompt Playground , and AlphaEvolve for HPC , and join us at our Advanced Computing Community Half-Day Workshop and Agentic and Hybrid HPC Birds of a Feather (BoF) session . To start building today, explore the Google Cloud Cluster Toolkit and Google Cloud HPC solutions . To access and implement these agentic workflows, download the validated templates from the Google Skills and DeepMind Science Skills repositories.
Bridging the gap between analytical systems (OLAP) and transactional databases (OLTP) is notoriously complex. Synchronizing data between the two systems, often called Reverse ETL (Extract, Transform, Load), traditionally requires heavy middleware, staging buckets, and complex orchestration, especially at enterprise scale. We’re announcing the general availability (GA) of BigQuery exports to AlloyDB , allowing data engineering teams to use a single, declarative SQL statement to export data from BigQuery to AlloyDB. This approach completely removes the need for complex ETL middleware. To demonstrate the benefits of this feature, we are sharing how Copel , a Brazilian electric power provider, eliminated a massive orchestration bottleneck and scaled their smart meter telemetry by migrating to high-performance, built-in Reverse ETL. The challenge of scaling smart meter telemetry At Copel, managing telemetry data from millions of smart electricity meters is a mission-critical operation. This data is streamed continuously and consolidated in an analytical Gold layer in BigQuery. However, to power Copel's "Systems of Action" — low-latency customer support dashboards, mobile applications, and operational billing systems — this rich dataset must be constantly synced back into its high-performance transactional database, AlloyDB for PostgreSQL. Until recently, Copel orchestrated this Reverse ETL architecture with a complex DAG in Google’s Managed Service for Apache Airflow. However, there were multiple brittle, intermediate stages in the process of enriching and exporting hundreds of millions of records from BigQuery to AlloyDB: Figure 1: Copel’s legacy multi-stage architecture requiring Cloud Storage file dumps and sequential gCloud scripts. BigQuery ran enrichment and extraction queries, dumping SQL results into dozens of fragmented CSV files inside a Google Cloud Storage bucket. Apache Airflow executed SQL to create temp tables in AlloyDB to prevent write locking or data corruption for production tables. Sequential gCloud scripts combed through the Cloud Storage CSVs to load data into a respective AlloyDB cluster and shards. Finally, a SQL transaction block safely dropped the production table and renamed the temporary table to production. To run this legacy architecture, Copel’s team needed to maintain the pipeline, carefully manage file staging, perform complex IAM impersonation across multiple services, and constantly monitor end-to-end system health. The breakthrough: Built-in Reverse ETL With BigQuery's EXPORT DATA to AlloyDB , Copel was able to replace this entire multi-step pipeline infrastructure with a single, elegant SQL statement. Now, BigQuery exports communicate directly with AlloyDB instances over highly optimized, parallelized connections — no staging files in Cloud Storage, gCloud scripts, or managing temporary tables required! Figure 2: New architecture showing direct parallel Reverse ETL connection between BigQuery and AlloyDB. Here is how simple the new Reverse ETL process looks in code: code_block )])]> The resulting BigQuery values map perfectly to AlloyDB's columns during the export, leaving the nested structures fully queryable and indexable on the PostgreSQL side. Evaluating performance and scalability To validate this new architecture, Copel executed two large-scale Reverse ETL benchmarks: comparing the performance and scalability of a smaller incremental Reverse ETL export against a much larger full-table Reverse ETL export. The performance results exceeded all initial expectations: Metric Test 1: Incremental Export Test 2: Full-Table Export Total Rows Exported 18,145,589 rows 207,484,577 rows Logical Data Size 2.32 GB 188.87 GB Export Throughput ~3.1 Million rows / minute ~4.2 Million rows / minute AlloyDB Specs N2 (16 vCPUs, 128 GB RAM) N2 (16 vCPUs, 128 GB RAM) CPU Utilization Hovered stably at ~85% Hovered stably at ~85% Pro-tip on ingestion : Sustaining an export throughput of ~4.2 million rows/minute on complex JSONB telemetry fields is an outstanding technical achievement. It highlights the low-level optimizations between BigQuery's distributed processing engine and AlloyDB's advanced storage layer. Key takeaways and benefits By adopting this declarative SQL approach, Copel successfully deleted dozens of lines of complex scripting and custom error-handling blocks. Their Airflow DAG now contains a single, simplified BigQueryInsertJobOperator task that executes the export query. This built-in approach unlocks several core business benefits: Direct cost reduction: By bypassing Cloud Storage entirely, there are no staging storage costs and associated API read/write operation fees. Enhanced data security: Data moves directly from BigQuery to AlloyDB inside Google's secure internal network without ever being dumped into readable CSV files. Near-zero maintenance: Pipeline errors are now contained within a single SQL statement, making troubleshooting and logging drastically simpler. Massive distributed writing : The direct export achieved write speeds exceeding 4 million records per minute by leveraging BigQuery's massive parallelism combined with AlloyDB's optimized PostgreSQL write performance. Now, rather than spending valuable engineering hours monitoring pipeline failures and intermediate storage buffers, data engineering teams can focus on what matters: delivering smart analytics and premium experiences to customers. Get started today: Modernize your Reverse ETL Now that BigQuery exports to AlloyDB are generally available, there has never been a better time to simplify your data architecture. If your team is struggling with complex, multi-stage pipelines to push BigQuery data into downstream OLTP databases, we invite you to try built-in export functionality for AlloyDB , Bigtable , Spanner , or Pub/Sub today.
Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. Tip : Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources . aside_block Nov 9 - Nov 13 More frontier model choice on Google Cloud: Anthropic’s Claude Fable 5.2 and Meta’s Muse model series. Developers now have even more flexibility to build, deploy, and scale with the right model for every workload on Google Cloud. Anthropic’s Claude Fable 5.2 is now generally available in the Model Garden, bringing frontier intelligence to complex coding, scientific research, and multi-agent orchestration. Plus, Meta’s newest models— Muse Spark 1.3 , Muse Wonder , and the efficient Muse Ember —are now available in public preview, giving teams seamless access to both proprietary and open-weight capabilities with enterprise-grade security. Explore the models in the model garden today. Next-Generation SecOps: SentinelOne and Google Cloud Google Cloud and SentinelOne joined forces to solve the threat scaling challenge with a fundamentally re-architected integration. As a Google Unified Security (GUS) Recommended endpoint detection and response (EDR) partner, SentinelOne pairs Google Security Operations through an optimized direct integration and streamlined data schemas, delivering essential endpoint telemetry visibility to SecOps detection and agentic SOC pipelines while significantly reducing the total cost of ownership of the integration. Read more Layered LLM guardrails on Apigee: Static rules, Model Armor, and NeMo Guardrails No single LLM guardrail catches every threat. A new technical guide and demo showcase how to use Apigee as an AI gateway to stack three complementary layers—deterministic static rules, semantic Google Cloud Model Armor filters, and flexible business logic via NVIDIA NeMo Guardrails. Together, they successfully intercepted 100% of benchmarked OWASP-mapped attacks without blocking safe prompts, running cheapest first to optimize performance and reduce token costs. Read the full guide Scale Gemma 3 reinforcement learning on GKE with NVIDIA NeMo RL, Custom ComputeClasses, and 8-NIC RDMA Learn how to run distributed Group Relative Policy Optimization (GRPO) post-training for Gemma 3 on Google Kubernetes Engine (GKE). This end-to-end blueprint pairs GKE Custom ComputeClasses (with DWS Flex-Start and Spot fallback) and 8-NIC RoCE RDMA networking with NVIDIA NeMo RL and vLLM V1 to improve Gemma 3 mathematical reasoning accuracy. Read the technical guide Securely connect internal Cloud Run MCP servers to Gemini Enterprise Learn how to connect Gemini Enterprise privately to internal StreamableHTTP Model Context Protocol (MCP) servers on Cloud Run using Egress Agent Gateway, Private Service Connect (PSC), FQDN-scoped Private Cloud DNS for Shared VPCs, and dual-header OAuth 2.0 + IAM authentication. Read the technical guide on Google Developer Forums and explore the reference implementation on GitHub . Building an adaptive AI gateway with Apigee for stateful multi-agent workloads Multi-agent workloads often suffer from compounding context bloat, retry loops, and cross-agent state bleed.. Learn how to use Apigee as a model-agnostic, stateful multi-agent control plane for Google ADK and Gemini Enterprise Agent Platform workloads. By enforcing turn-0 pre-warmed caching, in-flight trace compaction, tool loop recovery, and dynamic model tiering between Gemini Flash and Frontier models, teams can achieve up to 80% net FinOps cost savings. Read the full article to explore reference architectures and benchmark data . Anthropic’s exponential growth on Pub/Sub Discover how Anthropic, creators of the Claude AI models, uses Google Cloud Pub/Sub to power their cross-cloud telemetry and billing ingestion pipeline. Facing highly variable traffic and strict security mandates, Anthropic needed a secure, serverless messaging queue with zero manual partition management. By leveraging Pub/Sub with Private Service Connect, they built a highly efficient, zero-ETL architecture capable of seamlessly handling over 1 million events per second and 500 billion daily rows. Go behind the scenes in this case study to learn how Anthropic reduced consumer latency by 88% and achieved sub-second end-to-end event delivery. On-demand webinar: Architecting AI workloads with Pub/Sub Data engineering in the AI era demands fundamentally different infrastructure. Whether you're training a custom foundation model or deploying autonomous agents, you need a messaging backbone that handles bursty traffic with enterprise security and zero operational overhead. Dive into Pub/Sub’s capabilities for the AI era, reference architectures & best practices for emerging AI applications, and how Anthropic powers their telemetry pipelines with Pub/Sub in this free on-demand webinar . Nov 2 - Nov 6 Security for AI workloads with Model Armor Securing generative AI applications requires robust runtime protection against prompt injection and malicious inputs. Join Nicola Cardace on Thursday, November 19, 2026, at 5:00 PM CET for a Community TechTalk exploring how to safeguard enterprise AI workloads using Model Armor. Discover practical patterns for implementing active model defense across your LLM endpoints. Register for the November 19 Community TechTalk A5X accelerated by NVIDIA Vera Rubin NVL72 is now Generally Available Get the most out of A5X instances with Google Cloud’s AI Hypercomputer. Bringing together compute, storage, networking, and software co-designed as a single system. Read more Oct 26 - Oct 30 Simplify PostgreSQL Monitoring with the New AlloyDB Omni Web UI The newly released AlloyDB Omni Web UI delivers a centralized, graphical dashboard that simplifies the management of self-managed PostgreSQL workloads across hybrid, multi-cloud, and air-gapped environments. This browser-based console provides database administrators with deep, real-time insights into cluster health, hardware utilization, and active database connections. It also features built-in query diagnostics and performance tracking for AlloyDB’s columnar engine, enabling quick troubleshooting of bottlenecks without manual terminal commands. To learn more about setting up this monitoring interface, read the full AlloyDB Omni Web UI Blog Post . Leveraging "Antigravity" to architect high performance semantic model routers in Apigee Semantic model routing replaces rigid static paths with a lightweight helper model to dynamically classify and dispatch intent-driven LLM traffic. Join Saravanakumar Rajagopal on Thursday, November 12, 2026, at 5:00 PM CET for a Community TechTalk exploring how to leverage Antigravity and skills to architect these intelligent multi-provider routing patterns natively inside Apigee. Discover how to optimize your AI infrastructure and enhance flexibility at scale. Register for the November 12 Community TechTalk Oct 19 - Oct 23 From prompt to production: End-to-end AI workflows in Apigee Deploying an enterprise AI gateway does not require deep API or machine learning expertise. Using declarative workflows in Apigee, teams can spin up a fully governed LLM gateway in minutes using a few environment variables. Join Luis Cuellar on Thursday, October 29, 2026 for a Community TechTalk exploring end-to-end AI workflows. Discover how to layer essential defenses—including Model Armor for prompt protection, Cloud Data Loss Prevention (Cloud DLP) for sensitive data redaction, rate limiting, and response caching—before routing autonomous agent interactions through your gateway filters. Register for the October 29 Community TechTalk Zero Standing Privileges with PAM: On-Demand Temporary Access for Humans and AI Agents AI agents now hand work off to other agents, risking stacked privileges and overpermissioning. Because agents plan their actions on the fly, permanent admin access means one hallucination can cascade into production. Join us on Wednesday, November 18, 2026, at 8:30 AM PST to see how Google Cloud Privileged Access Manager (PAM) brings Zero Standing Privileges to AI delegation, putting humans in command with cloud-native control that doesn't slow developers down. Register for the November 18 webinar Streaming comes to API Gateway: real-time and AI-ready APIs Native streaming support is now in Public Preview for API Gateway, enabling you to run real-time and AI workloads behind your existing gateway. You can now route Server-Sent Events (SSE), WebSockets, gRPC, and chunked transfer encoding without modifying your API spec or managing a split estate. This update also strengthens integration with the Gemini platform, allowing you to stream responses from Gemini, Claude, and OpenAI gpt-oss models seamlessly while maintaining consistent authentication, rate limiting, and observability. Read the blog to get started Oct 12 - Oct 16 AI tokenomics in action: Enforcing tiered token budgets with Apigee Scaling artificial intelligence across an enterprise requires clear token governance to keep LLM spend predictable across departments. Join Tarun Abraham on Thursday, November 5, 2026 for a Community TechTalk on AI tokenomics. Discover how to use Apigee to enforce isolated, tiered token budgets and model access for each team—such as assigning Engineering a dedicated budget for Gemini Pro while covering mixed usage for Marketing—without changing application code. Attendees will also gain access to a sample codebase featuring a reusable Shared Flow. Register for the November 5 Community TechTalk Sept 28 - Oct 2 Claude Sonnet 5.5 is now available on Google Cloud. Built for focused coding and knowledge work, it delivers stronger performance for feature development, and creating presentation-ready documents, while offering more intelligence with a lower cost per task for most work at faster speed. Deploy Sonnet 5.5 directly from Model Garden with Google Cloud’s enterprise security, scale, and governance built in. Try it now . Mastering Storage Management with Storage Intelligence In our new blog, explore practical strategies to modernize storage operations using Storage Intelligence Advisor and enhanced Batch Operations. See how customers like Shipt and Palo Alto Networks are using these features to manage their estates at scale. Today, Storage Intelligence is used by 25 of the top 50 GCS customers. 2 in 3 customers have Storage Intelligence enabled for over 90% of their storage footprint. Explore best practices on our blog Cut Gen AI costs by 70% with Apigee X dynamic routing Generative AI deployments face a harsh cost-performance trade-off: static model selection either wastes budget or compromises quality. Discover how to build an Intelligent AI Gateway using Apigee X to dynamically evaluate prompt complexity in real-time. This new blueprint automatically routes simple queries to lightweight models (like Gemini 3.5 Flash Lite) and complex reasoning tasks to advanced models (like Gemini 3.7 Flash), achieving over 70% cost savings without sacrificing output quality. Read the guide to optimize your Gen AI architecture AI Agent Clinic launches new hands-on session on production agent evaluations A new episode of the AI Agent Clinic is now available showing organizations how to move past manual "vibe checks" to build robust, production-grade agent evaluations. In this hands-on session, Google Cloud engineer Dani Zamora and Matthew Feroz (Merge) take DocsHound, an open-source LangGraph agent, and build an end-to-end eval pipeline in under an hour. Learn how to standardize multi-turn traces across any framework using OpenTelemetry and OpenInference, pair LLM judges with deterministic checkers, and catch silent quality regressions before shipping. Watch here Drive AI impact: Join Google's Agentic Data Cloud event on Nov 4 Every enterprise plans to adopt agentic AI within two years, but data bottlenecks hold them back—AI accesses just 45% of enterprise data today ( MIT, 2026 ). Google's Agentic Data Cloud provides the trustworthy foundation and real-time context AI needs. Join our product leaders November 4th to explore the latest innovations across databases, analytics, business intelligence, and storage, and get direct answers in our executive Q&A with Google Data Cloud VP & GM Andi Gutmans. Register today! Sept 21 - Sept 25 Master MCP tool authorization and agent governance with Apigee While the Model Context Protocol (MCP) solves interoperability for autonomous AI agents, chained actions like CRM edits or database queries quickly expose systems to unauthorized execution. Join our technical deep dive on Thursday, October 1, 2026, at 5:00 PM CEST featuring Christophe from Google Cloud. Learn how positioning Apigee between MCP clients and enterprise backends enables fine-grained authorization (FGA), complete audit trails, and policy evaluation via emerging standards like OpenID AuthZEN. Language and accessibility note: This session will be hosted in French, but non-French speakers can follow along seamlessly by turning on Google Meet live translated captions to read in English, Spanish, German, Portuguese, or Italian. Register for the October 1 Community TechTalk Apigee Trace Viewer Tutorial: Capturing & Analyzing Proxy Traces Streamlining API proxy debugging just got easier with a new tutorial by Apigee Customer Engineer Tyler Ayers. The guide covers end-to-end instructions for capturing debug traces in both Google Cloud Apigee X (or Hybrid) and the local Apigee Emulator, extracting trace JSON data via the web UI or automated REST APIs, and analyzing execution flows, variable mutations, and latency bottlenecks using the open source Apigee Trace Viewer. Read the Apigee Trace Viewer guide today. Automate Apigee proxy testing locally Catching errors early saves time and money. A new tutorial by Apigee customer engineer Tyler Ayers shows how to use the Apigee Local Emulator for automated testing. Learn to run tests locally, integrate them into CI/CD pipelines, and deploy on Google Cloud Run for shared sandboxes. This approach provides instant feedback and zero cloud costs, helping teams speed up deployment cycles. Read the tutorial Scale your enterprise multi-agent systems with Apigee Deploying multi-agent architectures in production introduces critical hurdles around security, operational control, and runtime expenses. Discover how Apigee API Hub provides a central discovery surface to eliminate agent sprawl across tools, Model Context Protocol (MCP) servers, and enterprise APIs. Learn how to turn existing backend services into secure MCP tools using Agent Gateway guardrails, while applying semantic caching and intelligent model routing to keep compounding token costs predictable. Join Google Cloud in Chicago in Oct.15 for The AI Evolution. Reserve your seat for Chicago Automate Apigee proxy testing with the Apigee Local Emulator Waiting on remote deployments to validate API proxy logic slows down release cycles and increases infrastructure overhead. Join Nigel Walters on Thursday, October 8, 2026, at 5:00 PM CEST for a Community TechTalk on shift-left testing for Apigee. Discover how to use the Apigee Local Emulator and apigee-emulator-service to run sub-second assertion suites on local machines, automate CI/CD checks in GitHub Actions, and deploy ephemeral preview sandboxes on Google Cloud Run. Register for the October 8 Community TechTalk Now in Public Preview: AI-assisted EKS-to-GKE migrations with deterministic guardrails Migrating complex Kubernetes estates from AWS EKS to GKE is traditionally high-friction and error-prone. Now in Public Preview, GKE Agentic Migration is an open-source agent plugin that replaces ad-hoc LLM prompting with an AI-assisted migration workflow protected by deterministic guardrails. Running locally in your development harness, it indexes source IaC, maps cloud-specific primitives (such as Karpenter to Custom Compute Classes), and validates configurations offline—delivering reviewable pull requests and data-migration runbooks with zero live cluster mutations. Learn more in the announcement blog and try the plugin on GitHub . Claude Opus 5.5 is now available on Google Cloud. Built for everyday complex tasks, it delivers stronger agentic coding, research, and analysis while handling long-running work at a lower cost per token. Google Cloud continues to provide enterprise customers with broad model choice to build, deploy, and scale their AI agents securely. Try it here . Import Delta Lake tables with Dataflow Job Builder! Migrating to borderless Lakehouse just got a lot easier. You can now import Delta Lake tables stored in Cloud Storage using Dataflow Job Builder , a no-code/low-code interface for authoring Dataflow pipelines. Because Dataflow is a fully managed service, you are spared the overhead of provisioning and managing virtual machines. For step-by-step guidance, check out the documentation here . Sept 14 - Sept 18 Storage Intelligence Advisor for Google Cloud Storage is now GA Google Cloud Storage customers can now manage cloud storage more effectively with Storage Intelligence Advisor , delivering curated metrics, automated anomaly detection, and actionable recommendations right out of the box, with zero setup required. Advisor baselines activity across your projects and automatically detects four key anomalies: surges in operations, unexpected rises in cross-region egress, and spikes in errors. Each finding includes deep drill-down visibility into the resources driving the change, alongside prescriptive steps to remediate issues before they impact performance or cost. Learn more to get started with Storage Intelligence Advisor . Build private WebSockets from Apigee X to Cloud Run Real-time AI agents and streaming architectures often require persistent, bidirectional connections. A new implementation guide by Apigee Customer Engineer Joel Gauci demonstrates how to establish private southbound connectivity between Apigee X and Cloud Run. Using Private Service Connect (PSC) and a Regional Internal Application Load Balancer, teams can enforce API governance and security policies at the edge while keeping backend services completely isolated from the public internet. Explore the step-by-step guide and open-source code Connecting Gemini Enterprise Agent Runtime to Apigee with Private Service Connect Deploying autonomous AI agents often presents security, compliance, and cost challenges. A new reference guide details how to build an end-to-end, private architecture between Gemini Enterprise Agent Runtime and Apigee. This design helps protect internal backends and manage token quotas. Read the full community guide and deploy the code Discover what’s new and next in Apigee As enterprise architectures adapt to generative AI and autonomous workflows, Apigee is expanding its proven platform capabilities to support modern AI gateway use cases alongside traditional API management. Join our session on Thursday, September 24, featuring Apigee Product Manager Geir Sjurseth. Get an inside look at recent product releases, explore architectural patterns for securing models and agents, and bring your questions for the live Q&A. Register for the September 24 Apigee product update Managed Service for Apache Kafka supports clusters with public Internet access! With Managed Kafka public clusters , you can now produce and consume messages from clients outside your VPC—including your local machine, for faster, frictionless testing. Public clusters unlock use cases like IoT devices, retail storefronts, and telco network towers. Enable public access on new or existing clusters via the Google Cloud console, gcloud CLI, or REST API. Spin up your first public cluster , or reach out to kafka-hotline@google.com with questions. Stream data directly into Bigtable using Bigtable subscriptions, now in Preview! You can write Pub/Sub messages to a Bigtable table with zero ETL with Bigtable subscriptions . No pipelines, no code, delivered by the serverless, zero-ops experience you already know with Pub/Sub. Power your AI workloads, from model telemetry to real-time context engineering, without the overhead of managing complicated ETL pipelines. Built to be dependable, with native support for dead-letter topics. Try the feature today ! Sept 7 - Sept 10 Why Your Voice Agent Needs Session Auditing Moving voice agents to production demands robust quality monitoring. This guide dives deep into the inner workings of the Agent Development Kit (ADK) responsible for audio session auditing. Learn how the ADK's save_live_blob feature intercepts, buffers, and stores raw audio chunks during active Gemini Live sessions. We explore building an automated post-processing pipeline to seamlessly stitch these fragments into cohesive, playable audio files. Discover how to leverage these vital audio audit trails to monitor real-world interactions, diagnose failures, and ensure enterprise-grade reliability. Read the full guide here . AlloyDB Omni Red Hat RPM Orchestrator now Generally Available AlloyDB Omni Red Hat RPM orchestrator is now Generally Available. The AlloyDB Omni Red Hat RPM orchestrator offers a new way to manage PostgreSQL-compatible workloads on bare metal or VM platforms, combining the high performance of AlloyDB, access to generative AI features and Gemini models to build AI agents and applications, and full automation. The orchestrator simplifies cluster provisioning and lifecycle management by allowing you to define reference architecture specifications, customizable by adjusting instance parameters, node configurations, and networking options — discover all details in full blog post . Aug 31 - Sept 4 Automate VM guest software lifecycle with VM Extension Manager, now GA Google Cloud VM Extension Manager is now generally available, eliminating the need for custom startup scripts to manage guest OS extensions across Compute Engine fleets. Define declarative, project-wide policies that enforce desired software states across all regions and zones. Benefit from continuous drift detection with automatic self-healing, multi-zone phased rollouts with automated rollbacks on failure, and centralized fleet health visibility integrated with Cloud Monitoring. Explore VM Extension Manager documentation Assess Apigee migrations without a target environment Planning a migration to Apigee X or Hybrid? You can now assess your legacy Apigee Edge SaaS or OPDK environment earlier in your planning cycle. Using the updated --skip-target-validation flag in the Apigee Migration Assessment Tool, teams can generate a full inventory and establish scope baselines before target infrastructure or IAM credentials are provisioned. Read the guide to learn more. Claude Fable 5.1 is now available on Agent Platform . It brings performance improvements over Fable 5 across reasoning, full-lifecycle coding, multi-tool workflows, and knowledge work. Anthropic also announced Enterprise Frontier Safeguards, a solution that gives customers the option to safely deploy Anthropic’s most capable models while storing their data in cloud infrastructure they control. We continue to offer enterprise customers options across frontier models to build, deploy, and scale securely on Google Cloud. Aug 24 - Aug 28 Grok 4.6 is now available in Preview on Gemini Enterprise. xAI's most capable model, built for coding, agentic tasks, and knowledge work, Grok 4.6 joins Grok 4.3 and Grok 4.20 in Model Garden and becomes the flagship of the Grok family. It supports reasoning, function calling, and structured output for multi-step agentic workflows, and accepts text and image input. Get started today Empowering autonomous agents with advanced security governance AI agents offer incredible productivity gains, but granting them access to read emails, query databases, and trigger APIs introduces critical new security risks. In fact, 79% of tech leaders cite security and governance as their biggest challenge to scaling AI. Traditional tools are no longer enough to handle automated threats like prompt injection and dynamic permissions. Discover how forward-thinking enterprises are using secure-by-default design, agent identity governance, and human-in-the-loop controls to deploy agents with confidence. Read more 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 ! Aug 17 - Aug 21 Webinar: Agent Identity as the backbone for secure AI innovation An AI agent with a stolen API key looks identical to a legitimate one. As autonomous agents scale across enterprise systems, static credentials and legacy IAM policies can no longer keep up with machine-speed execution. Join Shaun Liu, Product Manager at Google Cloud, on August 27 at 1 PM ET to explore Google Cloud’s vision for unifying agent, human, and nonhuman identity into a workload-centric platform using verifiable cryptographic identities (SPIFFE, ID-JAG, OAuth). Register for the webinar now Aug 10 - Aug 14 Diagnosing Apigee Hybrid Cassandra Read Latency for Peak Performance Diagnose real-time Cassandra read latency and resolve API key verification bottlenecks in Apigee Hybrid with this step-by-step troubleshooting guide. Learn how to deploy a debugging client and query performance tables to maintain sub-millisecond response times. Read the Apigee Hybrid Cassandra Troubleshooting Guide Keep moving with agents! The All Things Agentic Hackathon is officially live. We're challenging builders to build next-generation agents that take on the busy work and handle the heavy lifting in the background using Gemini 3.5 and Google Cloud. Compete for your share of $190,000 in prizes, cash, and Google Cloud credits! Submissions are open from August 3, 2026, to August 31, 2026. Learn more and register . Sign up for GEAR to get exclusive updates and your badge. #AllThingsAgenticHackathon Accelerate PostgreSQL migrations using Gemini in Database Migration Service Enterprise database migrations often stall during the "last mile" of translating legacy stored procedures, triggers, and custom functions from Oracle or SQL Server. Database Migration Service (DMS) now provides AI-assisted code conversion powered by Gemini in Databases. By combining deterministic compiler rules for 1:1 syntax with Gemini contextual synthesis for complex procedural blocks, DMS converts legacy code into native PostgreSQL and AlloyDB with full schema awareness and side-by-side validation. Read the full blog post to learn how to streamline your database code conversion. Compute Flex CUDs now available for G2 and G4 GPU VMs Compute Flexible Committed Use Discounts (Flex CUDs) are now available for G2 (NVIDIA L4) and G4 (NVIDIA RTX Pro 6000) VMs . You can now lock in predictable savings while retaining the flexibility to adapt across VM families, migrate between regions, and combine general-purpose compute, GKE, Cloud Run, and G2 & G4 GPU VMs under a single spend commitment. Flex CUDs for G-series VMs let you lock in savings today while preserving the agility to upgrade to latest hardware without disruption! Explore VM instance pricing or learn more about Flex CUDs . Rapid Bucket accelerates the training and checkpoint performance in PyTorch Ecosystem via GCSFS With the release of GCSFS 2026.8.0 , organisations can now unlock maximum ROI from their AI/ML infrastructure by eliminating data starvation on GPUs in PyTorch ecosystem when they are using Frameworks like Dask, Pandas, PyTorch , PyTorch Lightning, Hugging Face Datasets, Ray dataetc. By making adaptive concurrent prefetching the default, GCSFS dynamically predicts and background-fetches sequential read patterns—boosting single-file throughput by 5x, and scaling up to 21 GiB/s , saturating the NIC when paired with Rapid Bucket . Saturating the NIC translates to significantly improved accelerator goodput and reduced training wait times with zero integration friction. Training and checkpoint restore workflows benefit from intelligent memory management that automatically drains the buffer during random reads to completely avoid bandwidth or memory penalties. Aug 3 - Aug 7 Navigate data sovereignty and AI innovation with hybrid cloud For enterprises facing strict compliance rules, keeping sensitive data on-premises often means missing out on cutting-edge AI. Data from the 2026 State of AI Infrastructure report reveals that 52% of IT leaders are adopting hybrid cloud strategies to bridge this gap. Our latest blog post explores how Google Distributed Cloud (GDC) helps organizations deploy connected or air-gapped models to run advanced AI entirely within secure environments—mitigating geopolitical risks without sacrificing innovation. Read more . SAP and Google Cloud Launch BDC Connect for BigQuery For years, enterprises have struggled with the cost, risk, and complexity of moving mission-critical SAP data into advanced analytics platforms. The general availability of SAP Business Data Cloud (BDC) Connect for BigQuery marks a turning point. By introducing revolutionary zero-copy, bi-directional data sharing, this new capability seamlessly bridges SAP systems with Google Cloud's powerful data and AI ecosystem. Instead of wrestling with manual data duplication and lost business context, organizations can now eliminate silos, dramatically lower their analytics costs, and rapidly deploy trustworthy, agentic AI solutions grounded in real-time operational reality. Read the full announcement to learn how to transform your data strategy . Google Cloud Cortex Framework version 7 is now generally available! This release helps you modernize your data architecture for AI agent readiness, enabling you to quickly deploy, customize, and extend robust data products while simplifying orchestration and reducing infrastructure overhead. It provides data product accelerators for SAP-sourced data to build trusted, high-quality data products ready for advanced analytics and agentic use cases. The Framework integrates with Google Cloud products including BigQuery , Dataform , Knowledge Catalog , and Gemini Enterprise Agent Platform . Learn more in our announcement blog , technical documentation , or try a demo deployment today. From API Management to AI Gateway with Apigee Massive LLM adoption unlocked automation but exposed critical vulnerabilities, from unpredictable token costs to security risks like prompt injection. Without central management, organizations face accelerated technical debt. Learn how to transform Apigee into an enterprise AI Gateway to centralize governance. This architectural roadmap details how to utilize semantic cache to optimize token costs, implement prompt protection policies for security, and productize tools using the emerging MCP standard. Read the full architectural roadmap on the Apigee Community Hub Centrally govern enterprise AI traffic with Apigee AI Gateway Manage, track, and secure model communication across your entire infrastructure from a single pane of glass. In a new video walkthrough, Principal Architect Tyler Ayers demonstrates how Apigee AI Gateway simplifies agentic governance. Learn how to transparently proxy model traffic, log real-time token counts, and apply runtime security quotas without impacting your developer workflow. Watch the Apigee AI Gateway demo Maximize Provisioned Throughput Utilization Sudden traffic micro-spikes can exceed per-second quotas, triggering 429 errors or forcing overflow into shared resource pools. A new architectural guide demonstrates how to build a serverless "shock absorber" using Cloud Run and Google Cloud Tasks. By decoupling request ingestion from execution, this queue-based pattern flattens volatile traffic bursts and smoothly drips requests to Gemini at your exact quota rate, maximizing Provisioned Throughput utilization while eliminating job failures during peak usage. Read the step-by-step setup guide . Eliminate security blindspots in agentic tool interactions Unmonitored agentic tool calls via the Model Context Protocol (MCP) can introduce critical security risks to your enterprise architecture. Join our technical deep dive on Thursday, August 13, to discover how to position Apigee as a centralized security gateway. Featuring the new ParsePayload policy and payload operations groups in API Products, this session demonstrates how to enforce granular tool filtering, manage execution quotas, and scale secure agent ecosystems without impeding developer velocity. Register for the August 13 Community TechTalk Jul 27 - Jul 31 Data Cloud and Apigee CDMX: The AI Agent Evolution | August 12, 2026 Enterprise AI demands evolution beyond basic conversational assistants. To generate real value, AI models must connect with the organization's core systems and live data sources. Join us this August 12 at Google CDMX for the exclusive event AI Evolution: Powering Tomorrow's Enterprise . Learn how to design an agile and secure ecosystem by unifying the power of Gemini, Apigee, and data agent technologies through practical demonstrations led by Google Cloud engineers. Secure your spot for the in-person session in Mexico City Register now! Vast Edge , built on GCP, launches the first live recovery interface for cloud backups, enabling IT teams to inspect backup contents in real time. This transforms backups from a blind, log-based process into an interactive platform where teams can instantly search, preview, and validate the exact data available for restore . This platform protects Google Workspace, NetSuite, Salesforce, Workday and many SaaS environments, providing complete visibility and enterprise-grade oversight. Visit Vast Edge Backup & Disaster Recovery and get a free trial of their backup solutions on the GCP Marketplace for Google Workspace Backup , NetSuite Backup , Salesforce Backup , and Workday Backup . Jul 20 - Jul 24 Claude Opus 5, Anthropic’s latest model, is now available on Agent Platform. It brings performance improvements over Opus 4.8 across coding, long-running agents, and knowledge work.The model is Zero Data Retention (ZDR) compatible. For safety, high-risk workflows — such as penetration testing or exploit generation — it will notify you and fall back to Opus 4.8.We’re excited to continue to offer enterprise customers options across frontier models to build, deploy, and scale AI securely. Try it here . Apigee Northam Roadshow 2026 | The AI Agent Evolution: Powering Tomorrow's Enterprise AI is evolving. As your organization deploys autonomous agents, the integration between APIs and models becomes critical. Join Google Cloud specialists for an exclusive day of deep-dive sessions and live demos. Discover how the unified power of Apigee and the Google Cloud Agent Platform allows you to build, govern, and scale high-performance AI agents with complete control. Call to Action: Register for Sunnyvale | Register for NYC | Register for Chicago Deploy an Apigee Proxy for MCP Registry Discovery Learn how to deploy an Apigee X proxy to format Apigee API Hub data into the Model Context Protocol (MCP) Registry format. This tutorial by Tyler Ayers guides developers through cloning the sample repository, deploying using the Apigee Feature Templater (aft), and testing the endpoint to make API data easily discoverable by coding agents. Read the full community tutorial to get started. Simplify AI Infrastructure: Getting Started with Apigee AI Gateway Managing a complex AI landscape with multiple backend environments can present significant operational and governance challenges. A new tutorial walks you through how to build a unified API proxy using Apigee AI Gateway. By establishing a single, secure entry point for all model traffic, teams gain access to real-time analytics, comprehensive tracing, and financial operations auditing—completely seamlessly, and with absolutely no modifications required to client environments or user configurations. Read the step-by-step setup guide Your AI agents are ready. Is your data? The biggest bottleneck to scaling AI isn't the models—it's giving them access to business context. As enterprises move to proactive systems of action, legacy infrastructure often buckles under the nonlinear speed of AI agents. Google Cloud’s new Agentic Data Cloud, built on AI-native infrastructure, solves this by unifying data, AI models, and operational databases. Discover how a borderless Lakehouse and active Knowledge Catalog can empower your AI agents with trusted, real-time context without unnecessary engineering overhead. Read more . Secure and govern your AI at Apigee AI Horizon in London Moving AI from basic prompts to complex agentic workflows requires trust and control. Join us on Tuesday, 1st September 2026 at Google London for our 5th edition of Apigee AI Horizon. Discover how Google Cloud product leaders and architects are using Apigee and Model Armor to secure LLM APIs, implement policy controls, and manage token consumption. Do not miss this one—register soon! Secure your spot for AI Horizon London Jul 13 - Jul 17 Resource-Based CUD Sharing is Now Enabled by Default Starting June 16, 2026 , the default setting for Google Cloud Resource-based Committed Use Discount (CUD) sharing will change from disabled to enabled for new billing accounts and eligible existing accounts without active CUDs. This update automatically maximizes your savings by pooling underutilized discounts across your resources. You retain full control and can adjust your CUD sharing preferences at any time by changing your CUD scope configuration. For instructions, see Enable CUD sharing or Disable CUD sharing . Webinar for India: Google Cloud for EdTech: Optimizing Traffic and Token Governance at Scale API traffic surges and AI model integration are reshaping the EdTech landscape. Join Satyam Maloo for the webinar Google Cloud for EdTech: Optimizing Traffic and Token Governance at Scale on July 23, 2026. Learn to implement advanced rate limiting, gain granular token visibility, and leverage real-time analytics to govern your platform effectively. Whether you’re scaling for peak academic seasons or integrating complex AI workflows, this session provides the infrastructure blueprint you need. Register Now Scaling AI Agents: Treat prompts like software artifacts As AI agents move into production, monolithic system prompts often result in configuration drift, merge conflicts, and silent runtime failures. The solution is adopting a Prompts-as-Code architecture. By breaking prompts into modular skill files and using a build-time transpiler, engineering teams can introduce dependency resolution, static validation, and CI/CD rigor to their agent's control plane. Stop manually editing massive text files and start building deterministic, reliable agent infrastructure. Read more here . Jul 6 - Jul 10 Webinar: Introducing Google Cloud NGFW Enterprise advanced malware protection - powered by Palo Alto Networks Discover the new Cloud NGFW advanced malware sandbox, arriving in preview later this year. Powered by Palo Alto Networks Advanced Wildfire, it leverages data from 70,000+ customers to help defeat advanced malware. Join us on July 16 at 11 AM EDT to learn how to build a resilient, zero-trust cloud infrastructure that protects your apps and data, wherever they reside. Register for the webinar now Safely run AI-generated code in Cloud Run sandboxes Cloud Run sandboxes, now in public preview, are lightweight, isolated execution boundaries that you can spawn near-instantly within your existing Cloud Run service instances . Whether you need to let an LLM run a dynamically generated Python script to calculate business margins or spin up a headless browser to perform web research, Cloud Run sandboxes give you a secure, isolated sandbox to run these tasks without leaving your serverless environment. Read the blog to learn more and get started today. Australia API Horizon: Scaling Enterprise Governed AI Agents The transition from AI chatbots to autonomous agents is the most critical integration point for your business. Join Google Cloud at our upcoming events to explore exclusive deep-dive sessions on architecting for the agentic era. Discover how to use Apigee as an intelligent AI Gateway to govern, secure, and scale high-performance architectures. You will learn to seamlessly build AI tools from your existing APIs and maintain control over your entire ecosystem. Join us in your preferred city: Sydney: July 28, 2026, at Google Sydney, One Darling Island. Canberra: July 29, 2026, at Hotel Realm. Melbourne: August 4, 2026, at Google Melbourne. Build highly available, multi-region services on Cloud Run Maintaining uptime for business-critical applications just got a lot easier on Cloud Run. Service health, now Generally Available, automates cross-region failover by leveraging readiness probes for instance-level health checks with a simple, two-click setup. You can configure service health with global external Application Load Balancers for public-facing applications or cross-region internal Application Load Balancers for private networking traffic. Learn how to configure service health for Cloud Run. Report: 83% of organizations need infrastructure upgrades for agentic AI The shift from conversational bots to autonomous agents is breaking legacy systems. Our new State of AI Infrastructure report details how engineering leaders are adapting to these massive new workloads. To eliminate inference bottlenecks, control hidden scaling costs, and manage agent sprawl, the industry is rapidly moving toward fluid compute, centralized governance, and unified, co-designed architectures. Explore our key infrastructure insights Stop tinkering, start scaling: the industrialized AI Playbook Did you know that only 5% of custom AI investments actually return measurable business value? The problem isn’t the technology—it’s how organizations are wired to run it. In this compelling read, Google Cloud Consulting breaks down the operational blueprint that bridges the stark gap between "cool tech experiments" and real, P&L-impacting enterprise ROI. Read the full article on Medium AI Agent Clinic: Slashing App Latency by 80% Prototyping an AI agent is easy, but scaling for live traffic presents unique challenges. In the latest AI Agent Clinic, our technical experts partner with a developer to optimize PlaybackIQ, a live football analysis agent. This session demonstrates how to use OpenTelemetry to trace bottlenecks in the Gemini Enterprise Agent Platform and deploy to Cloud Run for high-concurrency scaling, achieving an 80% reduction in response time. Learn production-grade debugging strategies to optimize your own LLM applications. Watch the 60-minute teardown Jun 29 - Jul 3 Claude Sonnet 5, Anthropic’s latest model, is now available on Agent Platform . This addition serves as a drop-in replacement for Sonnet 4.6, giving organizations expanded choice for task completion across enterprise workflows. It features enhanced reasoning, cleaner code generation, and computer use capabilities for desktop and browser workflows. By continuing to rapidly bring frontier models to our platform, Google Cloud offers an uncompromised choice of the industry's best technology to build, test, and scale enterprise-grade AI. Get started today. Automate your AI governance with Apigee and YAML Manual API gateway configurations can quickly slow down your AI engineering velocity. Join the Apigee community on Thursday, July 16, to discover an automated, declarative blueprint for model garden management. Learn how a simple, repeatable YAML pattern lets your AI practitioners instantly spin up secure, policy-backed enterprise configurations without friction. Bring your questions and connect during our live Q&A session. Register for the July 16 Community TechTalk Build next-generation AI portals for autonomous agents Standard developer portals were designed for human developers to subscribe to static APIs. Today, autonomous agents, LLM toolkits, and dynamic runtimes demand a central nervous system for governance. Join our technical deep dive on Thursday, July 23, to explore Apigee's new AI Portals solution. You will see exactly how to deploy full-service, MCP powered hubs to safely manage enterprise self-service for models, tools, and agents. Register for the July 23 Community TechTalk Protect your infrastructure from advanced cyberattacks at the API layer (Presented in Portuguese) In an era of increasingly sophisticated threats, relying solely on traditional firewalls leaves critical data gaps. Join our technical community TechTalk on Thursday, July 30—conducted in Portuguese—to learn how to proactively mitigate risks directly at the gateway layer. This session demonstrates how to configure and govern essential Apigee security policies to build a robust line of defense, ensuring maximum availability and complete integrity for your enterprise microservices. Register for the July 30 Portuguese Community TechTalk Jun 22 - Jun 26 Accelerate TPU model loading while saving RAM on GKE. Large model cold starts often stall scaling and leave high-value TPUs idle. The open-source Run:ai Model Streamer now natively supports TPUs with Google Cloud Storage in TPU vLLM 0.18.0 . This integration accelerates inference pipelines on GKE by streaming tensors directly into CPU memory, bypassing local disk bottlenecks and the "double-buffering" trap. In benchmarks, loading a 480B parameter model was over 2x faster while cutting peak host memory usage by half. Read the full guide and get started today . Stop Training Blind: Scaling AI with the New OpenTelemetry-Based TPU AI Telemetry Collector Agent Google Cloud’s new AI Telemetry Collector agent standardizes TPU monitoring using OpenTelemetry. It optimizes enterprise ML workloads by identifying silent failures and providing zero-cost operational metrics without draining host CPU cycles. The agent seamlessly routes telemetry to Google Cloud Monitoring or Prometheus and custom Grafana setups. Pre-installed on Google-optimized Ubuntu images or available via Docker, it tracks memory, network latency, and core utilization to maximize multi-node training efficiency. You can read more of this capability by clicking this link . Jun 15 - Jun 19 Join us for a deep dive into agentic AI control with AppyThings Your integrations aren’t failing—they are evolving. When users interact with AI agents, they no longer arrive directly at your site, resulting in experiences stripped of your context, expertise, and intended experience. Join us on Thursday, June 25, for a community tech talk in partnership with AppyThings to learn how to solve this new gateway challenge. We will explore how MTN laid an integration foundation with the Model Context Protocol (MCP) to deliver accurate, consistent experiences. Our technical experts will demonstrate how to leverage Apigee as a centralized tools management solution to govern agent access. Register for the session Optimize Spot VM Deployments with Capacity Advisor for Spot, Now in Public Preview Google Compute Engine has launched Capacity Advisor for Spot to Public Preview, now open to all customers. This tool turns Spot capacity discovery into a data-driven process by providing real-time deployment recommendations to maximize obtainability and minimize preemption risks. Query the Capacity Advisor API for obtainability and minimum estimated uptimes, or use the new Console UI featuring a global availability map, spot price lookups, and historical preemption rate trends to visually find the most cost-efficient compute capacity. Get started today to start optimizing your Spot VM deployments! Build a multi-tenant agentic AI system When scaling generative AI across different business units, your teams need specialized AI agents with unique operational rules and tools. Our new reference architecture helps you build a centralized multi-tenant platform to prevent fragmented silos, eliminate data exposure risks, and maintain unified compliance. Read the guide to design and deploy a multi-tenant agentic AI system in Google Cloud. How to Configure Gemini Enterprise to Connect to a Custom MCP Server The Gemini Enterprise MCP Connector was a big announcement at Google Cloud Next because it introduces the ability to connect Gemini Enterprise to MCP servers. This blog post provides a step-by-step guide on how to configure your first Custom MCP Server connector using the Google Maps Ground Lite MCP server as an example. Once you understand this flow, you can configure multiple MCP servers with Gemini Enterprise to bring all the context you need. Jun 8 - Jun 12 Simplify Multi-Cloud Planning with Cloud Location Finder, now Generally Available Cloud Location Finder provides up-to-date data on public regions, zones, and Google Distributed Cloud Connected locations across Google Cloud, AWS, Azure, and OCI. You can now programmatically discover locations based on provider, proximity, territory, and carbon footprint to optimize your global infrastructure strategy for performance, compliance, and sustainability. Get started for free today Jun 1 - Jun 5 Modeling the physical world with BigQuery Graph Managing complex supply chains requires more than just spreadsheets; it requires a digital replica of the physical world. In this post , Guru Rangavittal and Candice Chen explore how BigQuery Graph enables organizations to build a digital twin by turning physical assets into an interconnected map of nodes and edges. By moving beyond traditional relational databases, businesses gain real-time clarity into operations—from executing surgical ingredient recalls to analyzing weather-driven logistics risks. Discover how BigQuery Graph transforms reactive firefighting into proactive, precision modeling, allowing you to see critical connections in seconds and future-proof your supply chain. Apigee for AI: Govern LLMs and MCP Servers (Presented in Spanish) Learn how to securely transition your AI initiatives from experimental prototypes to enterprise-ready deployments. Join Luis Cuellar on June 18 for a technical deep dive (presented in Spanish) exploring Apigee’s latest AI gateway capabilities. Discover how to centralize governance over Model Context Protocol (MCP) servers, protect Large Language Models (LLMs) with robust API gateway security policies, and manage token-based quotas. Register for the June 18 Spanish Community TechTalk May 25 - May 29 Anthropic’s Claude Opus 4.8 is now available on Gemini Enterprise Agent Platform . As we continue to expand our platform's model offerings, this addition gives organizations more options for handling complex, multi-stage enterprise workflows. Claude Opus 4.8 brings strong capabilities in agentic coding, allowing developers to manage extensive refactors and tracking dependencies over extended sessions. API Horizon Munich July 6, 2026: Orchestrating the Next Era of AI and APIs Master the orchestration of next-gen AI and digital ecosystems. Join Google Cloud experts and DACH tech leaders on July 6 for an exclusive look at the Apigee roadmap, Agent Management, and Model Context Protocol (MCP). Gain real-world insights and connect with the regional integration community. Register now Securing AI Agents: The Extended Agent Gateway Pattern Learn how to prevent autonomous AI agents from invoking unauthorized APIs. Join Apigee Specialist Joel Gauci on June 4 for a technical deep dive into the Extended Agent Gateway pattern. This session covers enforcing Fine-Grained Authorization (FGA), implementing secure token exchange, and establishing Model Context Protocol (MCP) governance at the API gateway layer to protect enterprise backend services. Register for the June 4 Community TechTalk API-to-Agent Security: Exposing REST APIs to Gemini Enterprise via MCP Connect Gemini Enterprise agents to core data without creating security hazards. Join Google Cloud Specialist Nigel Walters on June 11 to learn how to instantly transform legacy REST APIs into secure Model Context Protocol (MCP) servers. We’ll cover how to safely register tools with Gemini while enforcing gateway-level guardrails like rate limiting and access control policies. Register for the June 11 Community TechTalk May 18 - May 22 Chinese Webinar | June 4: AI Command and Control As AI agents move from experimental pilots to core enterprise functions, governance has become a critical next step. Join Google Cloud on June 4th at 10:00 AM (Beijing Time) to learn how to build a secure AI management layer architecture. We'll explore how to develop governed MCP (Model Context Protocol) endpoints, manage tool access to enterprise data, and leverage robust audit logs to operationalize AI. This session also includes a practical demonstration of these governance frameworks on Google Cloud. Register here GCP Announces New Features to Benchmark and Optimize LLMs for On-Device Use Cases Deploying fine-tuned LLMs from GCP to edge devices like smartphones is complex due to fragmented hardware. Google AI Edge Portal bridges this gap, giving GCP developers the ability to test AI performance on 120+ Android devices, representing the full diversity of high, medium, and low tier smartphones on the market today. This week at I/O, we announced brand new capabilities to benchmark and debug LLM performance across these devices. Sign-up to utilize these new features in private preview today. May 11 - May 15 Build Your AI & MCP Control Tower for Universal Governance Master the future of agentic security with Apigee. Join our Community TechTalk on May 21 to discover how Apigee serves as a central "Control Tower" for the Model Context Protocol (MCP). We will explore how new JSON-RPC tool authorization enables fine-grained access policies across your organization, ensuring secure and scalable AI deployments. Whether managing internal tools or external users, learn to govern your agentic ecosystem with absolute precision. This session is designed for global coverage across EMEA and AMER regions. Register for the May 21 Community TechTalk Apr 27 - May 1 Master Your Launch: The Apigee Production Go-Live Checklist Ensure a secure launch with the Apigee production guide. Join Nicola Cardace on May 28 to explore security guardrails, including IAM roles, mTLS configurations, and encrypted KVM migrations. Scheduled at 11 AM EDT / 5 PM CEST to support EMEA and AMER teams, this TechTalk provides the technical roadmap you need to flip the switch with absolute confidence. Register for the May 28 Community TechTalk Transforming APIs into Governed Agentic Tools on the Google Cloud Agentic Platform Turn your APIs into secure, governed agentic tools on the Google Cloud Agentic Platform. Join Specialist Christophe Lalevée on May 7 for a technical deep dive into AI productization. Scheduled at 5 PM CEST / 11 AM EDT to maximize coverage for developers across EMEA and AMER, this session explores the integration and governance frameworks required to scale enterprise-ready AI with confidence. Register for the May 7 Community TechTalk Fractional G4 VMs are Generaly Available, providing a highly efficient and cost-effective entry point for AI and graphics workloads. These new configurations, using NVIDIA virtual GPU (vGPU) technology, allow you to leverage the power of the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs in flexible, smaller increments, so you can right-size your infrastructure to match the specific demands of your applications. By providing more granular access to advanced hardware, fractional G4 VMs let you optimize resource allocation and reduce overhead without sacrificing performance. You can now select from additional GPU slice sizes for your specific needs: 1/2 GPU: Ideal for more intensive tasks such as LLM inference, robotics sensor simulation, and high-fidelity 3D rendering. 1/4 GPU: Optimized for mainstream workloads, including mid-range creative design, video transcoding, and real-time data visualization. 1/8 GPU: Great for lightweight applications such as remote desktops, productivity tools, and entry-level streaming services. Transitioning AI from a sandbox prototype to an enterprise-grade system is a major hurdle. A monolithic script won't suffice for widespread deployment. To achieve true scale and reliability with Gemini, organizations must adopt service-oriented micro-agent architectures, establish Zero-Trust security, and implement rigorous EvalOps. Master the "Agentic Maturity Ladder" to ensure your AI & Agentic solutions are robust, secure, and ready for the real world. Watch the deep dive and read the developer blog to learn more. ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available Data scientists and developers can now combine the local productivity of VS Code with the scalable infrastructure of Google Cloud. The new Google Cloud Workbench Notebooks extension allows you to connect to and run notebooks on managed cloud environments directly within your local IDE. This integration streamlines the ML lifecycle by eliminating context switching and providing high-performance compute for complex workloads in a familiar interface. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation. Install from Marketplace: GoogleCloudTools.workbench-notebooks Contribute on GitHub: colab-enterprise-vscode Apr 20 - Apr 24 Announcing the 2026 Google Cloud Partners of the Year Google Cloud is honored to celebrate the winners of the 2026 Partner of the Year awards! These awards recognize an exceptional group of partners across AI, Security, Infrastructure, and more, who have demonstrated a commitment to customer success. From global system integrators to specialized startups, these winners are leveraging the power of Google Cloud to solve complex challenges and drive digital transformation worldwide. Join us in congratulating these organizations for their innovation, collaboration, and impactful results over the past year. See the 2026 Partner Award winners Apr 13 - Apr 17 We're excited to announce the Public Preview of Datastream’s metadata integration with Knowledge Catalog . This is the first step in our vision to provide a centralized, "single pane of glass" for all Datastream assets. The enhancement automatically synchronizes Streams, Connection Profiles, and Private Connections, eliminating data silos. It enhances discoverability, allowing you to search for Datastream assets using the same interface as BigQuery tables. Centralized governance is also provided, making your real-time data estate more transparent and easier to manage. Upgrading Apigee OPDK to 4.53 with OS Modernization Modernize your infrastructure using Google’s official, sequential upgrade path. Our Technical expert, Rakesh Talanki outlines how to upgrade Apigee OPDK to v4.53 while migrating to a supported OS (RHEL 8.x/9.x). This guide covers the "build-out" methodology, including multi-data center syncing, to ensure a stable, zero-downtime transition Read the guide Cloud Run Worker Pools and CREMA: Powering Serverless AI at Scale Google Cloud has announced the General Availability of Cloud Run worker pools , a new resource type designed specifically for pull-based, non-HTTP workloads. Unlike traditional Cloud Run services that scale based on request traffic, worker pools provide an "always-on" environment for background tasks like processing message queues or running large-scale AI inference. To support this, Google Cloud also open-sourced the Cloud Run External Metrics Autoscaler (CREMA) . Built on KEDA, CREMA enables queue-aware autoscaling for worker pools, allowing them to dynamically scale based on external signals like Pub/Sub backlog or Kafka lag. Apigee Model Context Protocol (MCP) now Generally Available Expose enterprise APIs as MCP tools for agentic AI applications with the General Availability of MCP in Apigee. This update allows developers to transform APIs into AI-ready tools using OpenAPI Specifications, removing the need for local MCP servers or additional infrastructure. With managed endpoints and semantic search in API hub, you can now provide AI agents with secure, governed access to enterprise data at scale. Explore the MCP overview Apr 6 - Apr 10 Community TechTalk: Powering Retail Agents with ADK, UCP & Apigee X Move beyond basic chatbots to secure, transactional AI experiences. Join our Community TechTalk on April 16 to learn how Apigee X and Gemini build a "Trust Layer" for AI shopping assistants using UCP standards. We’ll demonstrate how to block prompt injections with Model Armor and implement cost governance via token limits to secure the path from discovery to purchase. Register for the TechTalk Implement multimodal capabilities in your AI agents Explore three new reference architectures for building sophisticated multi-agent AI systems that can process and analyze multimodal data. To analyze disparate multimodal data and produce a high-confidence classification, see Classify multimodal data . To create a fluid conversational AI that processes audio and video streams in real time, see Enable live bidirectional multimodal streaming . To consolidate fragmented multimodal data into a searchable knowledge graph, see Multimodal GraphRAG resource orchestration . Automate SecOps workflows with an agentic AI system To accelerate incident response and reduce manual toil for your security team, you need a system that can automate remediation playbooks. Our new reference architecture helps you build an AI agent that orchestrates complex triage and investigation workflows across disparate security tools, such as SIEM, CSPM, and EDR, from a single interface. See the full guide to orchestrate security operations workflows . Mar 30 - Apr 3 ASEAN Webinar | April 30: Mastering Agentic Governance at Scale with GCP As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud experts Shilpi Puri & Wely Lau for a webinar on April 30th at 11:00 AM SGT to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud. RSVP here. Mar 23 - Mar 27 Turn your API sprawl into an agent-ready catalog As organizations scale, APIs often become scattered across multiple gateways, creating "blind spots" that hinder AI adoption. To solve this, we’ve introduced two new capabilities for Apigee API hub: a new integration with API Gateway to automatically centralize API metadata into a single control plane, and a specification boost add-on (now in public preview). This add-on uses AI to enhance your API documentation with the precise examples and error codes that AI agents need to function reliably. Read the full blog post to get started. Webinar | April 16: AI Command & Control As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud expert Satyam Maloo for a webinar on April 16th at 11:00 AM IST to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud. RSVP here. Modernizing and Decoupling Event Ingestion with Apigee In modern cloud-native architectures, decoupling producers from consumers is critical for building resilient systems. While Google Cloud Pub/Sub provides a scalable backbone, exposing it directly to external clients can introduce security and management overhead. This new guide explores how to leverage Apigee as an intelligent HTTP ingestion point. Learn how to handle security, mediation, and traffic control before messages reach your internal bus using the PublishMessage policy or Pub/Sub API. Read the full guide. Mar 16 - Mar 20 Gemini-powered Assistant in BigQuery Studio Gets Context-Aware Upgrades The Gemini-powered assistant in BigQuery Studio has been transformed into a fully context-aware analytics partner, supporting your entire data lifecycle. The new capabilities include intelligent resource discovery, which uses Dataplex Universal Catalog search to find resources across projects and deep dive into metadata using natural language. You can now automate tasks, such as scheduling production-grade queries directly through the chat interface, and instantly troubleshoot long-running or failed jobs with root cause analysis and cost control auditing. Explore the full range of what the assistant can do. Mar 9 - Mar 13 Want to use Gemini to develop code and don't know where to start? This article includes a couple of examples of developing code with Gemini prompts; it identified changes that were needed to be made to get the code working. The article also refers to other examples that are available on github. Mar 2 - Mar 6 Introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Gemini 3.1 Flash-Lite can tackle tasks at scale, like high-volume translation and content moderation, where cost is a priority. And it can also handle more complex workloads where more in-depth reasoning is needed, like generating user interfaces and dashboards, creating simulations or following instructions. Starting today, 3.1 Flash-Lite is rolling out in preview to enterprises via Vertex AI and developers via the Gemini API in Google AI Studio . TechTalk: Implementing Device Authorization Grant (RFC 8628) for Apigee Learn how to authorize "headless" devices like Smart TVs or AI agents that lack keyboards and browsers. Join our Community TechTalk on March 19 (5PM CET / 12PM EDT) to go under the hood of Apigee X/Hybrid. We’ll cover the real-world mechanics of state management, polling, and human-in-the-loop security patterns for devices and autonomous agents. Register for the TechTalk Feb 23 - Feb 27 Pro-level image generation gets faster and more accessible with Nano Banana 2 Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here . The Intelligent Path to Compliance: Transforming Regulatory QC with Google Cloud Reducing "Refuse to File" (RTF) risks and submission cycle times is critical for life sciences leaders. Google Cloud’s Regulatory Submission Semantic QC Auditor leverages Gemini and RAG architecture to transform Quality Control from a manual burden into an active, intelligent workflow. By automating semantic cross-referencing, narrative coherence checks, and dynamic guidance-based auditing, this solution ensures rigorous accuracy and auditability. Operating within a secure GxP-ready environment, it empowers teams to detect subtle inconsistencies and generate remediation plans without sacrificing data privacy. Learn more . Stop typing, start interacting! The Gemini Live Agent Challenge is here . Build immersive agents that can help you see, hear, and speak using Gemini and Google Cloud. Compete for your share of $80,000+ in prizes and a trip to Google Cloud Next '26! Submissions are open from February 16, 2026 to March 16, 2026. Learn more and register at geminiliveagentchallenge.devpost.com Feb 9 - Feb 13 Introducing Gemini 3.1 Pro on Google Cloud. 3.1 Pro is a noticeably smarter, more capable baseline for complex problem-solving. We’re shipping 3.1 Pro at scale, building upon our goal to help you transform your business for the agentic future. Learn more about the model’s capabilities here . Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise . Developers can access the model in preview via the Gemini API in Google AI Studio , Android Studio , Google Antigravity , and Gemini CLI . Automate Storage Compatibility with GKE Dynamic Default Storage Classes Managing storage across mixed-generation VM clusters in GKE just got easier. With the new Dynamic Default Storage Class , Google Kubernetes Engine automatically selects between Persistent Disk (PD) and Hyperdisk based on a node's specific hardware compatibility. This abstraction eliminates the need for complex scheduling rules and manual pairing, ensuring your volumes "just work" regardless of the underlying infrastructure. By defining both variants in a single class, you reduce operational overhead while maintaining peak performance and cost-efficiency across your entire cluster. Explore automated disk type selection Community TechTalk: AI-Powered Apigee Development with strofa.io Join the Apigee community on February 26 for a deep dive into strofa.io . Guest speaker Denis Kalitviansky will demonstrate how this new AI-powered tool automates and orchestrates Apigee development, from local emulators to large-scale hybrid environments. Discover how to scale your API management and streamline team collaboration using the latest in AI-driven automation. Register now to reserve your spot. Jan 26 - Jan 30 Simplify API Governance with Native OpenAPI v3 Support Eliminate integration debt and accelerate deployment velocity with the General Availability of OpenAPI v3 (OASv3) support for API Gateway and Cloud Endpoints. You no longer need to downgrade modern specifications to OASv2. Instead, you can now define API contracts and enforce critical policies—including telemetry, quotas, and security—using native Google-specific extensions directly within your OASv3 files. This update ensures your APIs are secure by design while remaining fully compatible with the modern developer ecosystem and Google Cloud’s AI services. Get started with OpenAPI v3 on API Gateway and Cloud Endpoints. Accelerate API Testing with the New Open Source API Tester Start validating your APIs with API Tester, a simple, YAML-based Test Driven Development (TDD) framework. Designed for the Apigee community, this tool allows you to write human-readable tests, run them instantly via a web client or CLI, and perform deep unit testing on Apigee proxies. With native support for JSONPath assertions and Apigee shared flows, you can verify everything from payload data to internal variables like proxy.basepath without leaving your terminal. Explore the API Tester guide and start testing your proxies today. Secure Sensitive Data with Kubernetes Secrets in Apigee hybrid Enhance security in Apigee hybrid by accessing Kubernetes Secrets directly within your API proxies. This hybrid-exclusive feature keeps sensitive credentials within your cluster boundary and prevents replication to the management plane. It supports strict separation of duties: operators manage secrets via kubectl , while developers reference them as secure flow variables—ideal for high-compliance and GitOps workflows. Implement Kubernetes Secrets in your hybrid proxies. See the Console in a Whole New Light: Dark Mode is Now Generally Available in Google Cloud Elevate your cloud management workflow with Dark Mode, now generally available in the Google Cloud console. We have delivered a modern, cohesive, and accessible experience reimagined for maximum comfort and productivity—especially during extended working hours and low-light environments. Dark Mode can be enabled automatically based on your operating system's preference, or manually through the Settings -> Appearance menu. Switch to Dark Mode today to enjoy a modern, comfortable, and productive environment! Apigee X Networking: PSC or VPC Peering? Deciding how to connect Apigee X? Watch this video to compare Private Service Connect and VPC Peering. We break down northbound and southbound routing, IP consumption, and how to reach targets on-prem or in the cloud. Learn to simplify your architecture and avoid common networking "gotchas" for a smoother deployment. Watch the video. Jan 19 - Jan 23 Bridge the Gap: Excel-to-API Conversion in Apigee Portals Give your customers more ways to connect! This new article by Tyler Ayers explores how to extend the Apigee Integrated Portal to support direct Excel file uploads. By leveraging SheetJS and custom portal scripts, you can enable users to upload spreadsheets, preview data, and submit it directly to your APIs, all without writing a single line of integration code themselves. It’s a powerful way to simplify onboarding for those who aren't yet API-ready. Learn how to build it . Elevate your applications with Firestore’s new advanced query engine 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. Learn more about Firestore pipeline operations.
Spend Management adds opt-in spending caps for Cloudflare Workers and Durable Objects, and more products soon. Here is how we built the metering pipeline that turns usage into runtime decisions.