New capabilities to power agents at scale with Google's Agentic Data Cloud
Google Cloud Blog · 软件开发
Not long ago, we were entering the agentic era; today, organizations are deriving significant value from running agents in production. The opportunity to capture new revenue streams, improve efficiency, and manage risk makes reimagining operations with agents top of mind for enterprise leaders.
That is why we built the
Agentic Data Cloud
— a System of Action designed to activate all your data by breaking down walled gardens, deliver high accuracy and trust, and operate proactively at low cost. Leading organizations such as
PayPal, Siemens, Telus, Vodafone, Walmart,
and more are already achieving business impact with us today.
Today, we’re sharing key updates to Google’s Agentic Data Cloud. We’re empowering your agents everywhere, grounding them in your enterprise context to deliver accurate, trusted answers, and providing elevated agentic-first experiences for both practitioners and business users.
Activate all your data for agents with the borderless Lakehouse
Enterprise data lives everywhere — across multi-cloud environments, on-premises systems, and SaaS platforms like Confluent, SAP, Salesforce, and Workday. The
borderless Lakehouse
, built on the open Apache Iceberg format, lets you activate all of your data, wherever it lives, so that your agents have one governed foundation. Instead of moving data, stitching together connectors, or paying surprise variable egress fees, you get a stack tuned at every layer, from the infrastructure to the model. In fact, Apache Iceberg data under management has more than tripled over the past year.
Today, we are announcing several new enhancements for the borderless Lakehouse.
Gemini now works
directly on your borderless Lakehouse
allowing business users to use natural language to query records that live across clouds while agents ground their reasoning and drive business workflows. With
cross-cloud interconnect and caching for AWS and Azure
, now generally available, you can query data across clouds with a private, secure connection and zero variable egress fees. And when you query remote cloud data, Lakehouse automatically caches retrieved data blocks locally within Google Cloud. Additionally, customers can use the open Iceberg catalog federation with Databricks Unity, Snowflake Horizon, and AWS Glue as well as zero-copy integrations with SAP, Workday, and Salesforce.
To deliver context for AI, the
borderless Lakehouse integrates directly with Knowledge Catalog
, to automatically index your distributed and unstructured data, in cloud storage, giving agents the right context to be accurate, while controlling token costs for an intelligent and sustainable agentic strategy
.
"By implementing the borderless Lakehouse that federates single-copy Apache Iceberg data from Amazon S3 tables to BigQuery, Delivery Hero's talabat reduced data-movement costs by approximately 40% while making streaming transactional events queryable across both clouds within minutes of production."
-
Harish Ramesh, Staff Data Engineer, Delivery Hero talabat
To take action in the moment, agents need simultaneous access to both your historical analytical data, and real-time transactional data. That’s why today we're announcing new
AlloyDB
integrations with the borderless Lakehouse
. With
direct Iceberg access in AlloyDB
, you can now directly query Iceberg tables stored in cloud storage, without any ETL pipelines or copies, and join live operational data with historical lakehouse data in a single PostgreSQL query. By combining AlloyDB's transactional capabilities with direct Iceberg table access, systems can efficiently bridge transactional and analytical data to meet rigorous demands from agents. It is fully integrated with the
Lakehouse runtime catalog
, allowing you to register Iceberg tables in AlloyDB alongside the rest of your tables. Everything automatically shows up in Knowledge Catalog — so your agents get the same governed context. AlloyDB direct Iceberg access is up to 7x faster on aggregation queries than Amazon’s comparable PostgreSQL-compatible service.
And completing this borderless strategy,
Spanner Omni
, which is generally available, brings Google-grade consistency, scale, and multi-model capabilities to hybrid agentic applications across on premises, edge nodes, and multi-cloud environments.
Deliver accurate, trusted answers by grounding every agent in enterprise context
Agents are only as accurate as the business context they are grounded in.
Knowledge Catalog
is Google Cloud’s universal context engine that turns raw data into shared business context for agents to answer and act with higher accuracy, trust, and token efficiency. Today, we’re taking Knowledge Catalog to the next level
by extending our AI-driven context curation capabilities with
self-learning, reusable context sets, and a reimagined user experience
so your context layer is built and evolved automatically with your business while keeping humans in full control of their data governance. Based on our internal benchmarks, Knowledge Catalog makes AI agents nearly 4x more accurate, with up to 33% lower latency, and using up to 75% fewer tokens.
Much of an enterprise’s institutional knowledge lives outside their databases in operational playbooks, documents, and tickets, and context for agents must be curated around the questions people actually ask. With the new
self-learning enrichment
in Knowledge Catalog, available in preview, agents take on the heavy lifting of context curation. Knowledge Catalog learns from user intent and every human and agent interaction by continuously mining unstructured sources like Google Workspace, GitHub, Jira, Confluence, and SharePoint to close context gaps and evolve context automatically. It also packages domain knowledge into reusable
Open Knowledge Format
(OKF) context sets, allowing context engineers and data stewards to govern this enriched context effortlessly. For data stewards and data teams, we also introduced, a
new studio experience in Knowledge Catalog
,
in preview, which provides a conversational user interface where you can explore context in natural language, visualize your entire data estate across cloud environments and SaaS applications, and review, audit, and approve proposed enrichment updates with full versioning and rollback.
New studio experience in Knowledge Catalog showcasing conversational context exploration and an interactive graph
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