As enterprises shift their AI usage from experimental chatbots to autonomous agents, teams are hitting a critical bottleneck: accessing and reasoning over data scattered across multiple clouds, SaaS platforms, and on-premises systems. To turn AI agents into true systems of action, organizations need a foundation that can handle petabyte-scale reasoning without the security risks of data movement. Last week at our Gemini at Work event, we announced how Gemini can help you transform business workflows. To help power your agents with your enterprise data, we will dive deeper into the new Gemini integration with borderless Lakehouse to help you build, secure, and scale agents that work across your data estate.

The ultimate value of the borderless Lakehouse is realized through the agents you build to transform your organization, with Gemini as the front door. With the new Gemini integration, data that’s either preconfigured or stored in your borderless Lakehouse is now directly integrated into Gemini. Currently in preview, this means you can set up access to new data sources, such as Databricks Unity Catalog or Snowflake Horizon, through Lakehouse catalog federation directly from Gemini.

Now your business users can use natural language to query records that live across clouds while your AI agents can ground their reasoning and generate code from the same data — all without moving it. By directly contextualizing AI agents across your borderless Lakehouse, you can help accelerate your organization’s AI adoption while simplifying access to critical information.

In addition to this new, native integration with Gemini, we’re sharing that several features we introduced earlier this year to power your data foundation with the borderless Lakehouse are now generally available, including:

Support for Cross-Cloud Interconnect and cross-cloud caching for AWS and Azure

BigQuery support for Apache Iceberg V3 specifications

Read-write interoperability on Apache Iceberg tables for both BigQuery and Managed Service for Apache Spark

Bi-directional caching with AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog using Lakehouse runtime federation