Additionally, we announced that Knowledge Catalog is now on by default in Gemini, in preview. Every data question is now grounded in a governed business context automatically. When you connect BigQuery, AlloyDB, Cloud SQL, or Spanner, Knowledge Catalog resolves business terms, locates trusted tables, and determines join paths before generating SQL — under each user’s own access permissions. This gives organizations the confidence to deploy agents at enterprise scale because your agents draw from shared, registered context sets for consistent accuracy, lower latency, and reduced token costs.
Finally, because critical business definitions cannot be left to an LLM’s guesswork, Knowledge Catalog grounds agents in trusted semantic models, now generally available, from BigQuery, Looker (LookML), Databricks, Snowflake, dbt, and SAP, without moving data. Whether an agent is calculating net revenue for finance or customer churn for operations, it applies the exact same governed formulas your business teams rely on. Knowledge Catalog also extends metadata discovery and federation to Oracle AI Database@Google Cloud, Snowflake, Databricks Unity Catalog, and third-party catalogs like Atlan and Collibra — building on the catalog investments you’ve already made so your entire data estate is universally understood by every agent.
Transforming practitioner experiences with agents
We are reimagining how data professionals and business teams work — elevating practitioners from individual contributors supported by co-assistants, into orchestrators of autonomous agents that drive intent-driven business outcomes.
For both technical teams and everyday business users, we introduced two new purpose-built capabilities in Gemini, at the recent Gemini at Work event. For Data and ML engineers, we are extending Gemini with machine learning skills and tools that back every data scientist and data engineer with a team of agents. Engineers describe the outcome they want in plain language, and Gemini generates PySpark code, provides notebooks to edit and test it, trains models, and troubleshoots and fixes pipeline issues on its own. And for business users, anyone in your organization can generate reliable, real-time operational reports simply by asking. Gemini uses operational reporting skills integrated with BigQuery and the Knowledge Catalog to construct and save the query. Once saved, teams can run reports on demand without incurring token costs, guaranteeing consistent, verified answers every time.
Now, for developers who’d rather stay in their own tools, we recently announced the general availability of the Data Agent Kit. It eliminates console hopping and ETL boilerplate by integrating Google Data Cloud tools directly into your developers’ preferred IDEs and CLIs — like VS Code, Claude Code, and Antigravity. Armed with native MCP tooling, practitioners define business intent and let agents generate, tune, and test data pipelines.
Engineering a data infrastructure built for agent scale
As organizations scale to millions of agents collaborating across workflows, traditional database architectures break down. We’re introducing tailored infrastructure engineered specifically for agent scale.
To accelerate rapid testing and prototyping, today we’re announcing AlloyDB Express, in preview. This is a new edition of AlloyDB designed for builders and coding agents who need extreme development velocity. It offers sub-2-second instant provisioning, scale-to-zero pause and resume, secure private IP connectivity, integrated observability, and a small footprint with 1–2 vCPUs and 20GB of storage. AlloyDB now provides a developer-first, PostgreSQL environment for agent development and testing.
For production environments, we recently announced PostgreSQL for agents in AlloyDB, in preview, which enables thousands of collaborating AI agents to query live transactional data with complete workload isolation. With the agentic database architecture, agents achieve sub-millisecond I/O, and terabit-per-second aggregated scan throughput, supporting over 3 million queries per second. Compute scales automatically based on active reasoning sessions and scales back to zero when idle without affecting production systems.
For running open-source data infrastructure, Managed Service for Apache Spark extends its price-performance advantage compared to market alternatives by over 3x through further improvements to Lightning Engine. Recent enhancements for Spark 4.0 include faster startup at no additional cost, beneficial for interactive workloads and small batch ETL jobs. In addition, the premium tier provides performance boosts by pairing upgraded hardware with software acceleration from the Lightning Engine, and this is on by default.
Finally, we’ve continued to enhance BigQuery for agent scale. In the past year alone, we’ve made queries over 40% faster while cutting query processing cost by more than 50% through intelligent optimizations like fluid scaling and built-in workload acceleration. BigQuery Graph is now generally available, bringing petabyte-scale graph analytics directly on relational tables with zero ETL. It allows organizations to model data as nodes and edges to uncover complex, hidden relationships without moving data into a separate graph database. And today, two new models from Google Research run natively in BigQuery. The first is TimesFM 3.0, now in preview, which lets you generate forecasts directly in SQL with no custom models to train or manage and TabFM, also now in preview, which brings predictive machine learning directly into BigQuery.
Get started today
In addition to these announcements, we continue to enhance core performance, cost management, and operational resilience across our broader data ecosystem.
Ready to transform your data estate into an active System of Action? Explore our Agentic Data Cloud page, sign up for a Data Strategy Workshop and start building your agentic enterprise, at no cost, with Google Cloud.
You can also head over to our Trusted Agents, Real Outcomes digital event to catch our Agentic Data Cloud vision and the latest innovations. See the portfolio in action, learn from Yahoo’s real-world blueprint, and unpack deep-dive sessions with product leaders across Databases, Data Analytics, and BI.
