New capabilities to power agents at scale with Google's Agentic Data Cloud

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, K

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