Google’s Agentic Data Cloud to power ‘systems of action’

The days of the glorified artificial intelligence (AI) chatbot are over, with more enterprises starting to embrace the era of autonomous agents rummaging through corporate data.

At the recent Google Cloud Next 2026 conference in Las Vegas, Google took the wraps off its Agentic Data Cloud, a rebranding and architectural effort to support the transition from passive systems of intelligence to autonomous systems of action.

“Everyone’s talking about systems of intelligence, but frankly, they’re still very much kind of ingesting all the data, very much looking at the past, or maybe trying to predict the future,” said Andi Gutmans, general manager and vice-president for Data Cloud at Google.

“We’re moving from human scale to agent scale, both in the number of agents we’re going to have and also the workloads we have to manage,” he told Computer Weekly, referring to the exponential growth in compute requirements of agentic AI.

To address the workload, cost and governance issues associated with agentic AI, Google unveiled about 80 new product updates, focusing on metadata management, cross-cloud interoperability, and distributed database capabilities.

A primary concern for enterprise data teams deploying AI agents is ensuring models access the right data while respecting access controls. Gutmans noted that simply knowing where data resides no longer suffices – agents need semantic context to avoid hallucinations and errors.

“It’s typical for customers to have 500 customer tables, but which table do you want to look at?” Gutmans said. In response, Google launched the Knowledge Catalog, which builds upon its previous Dataplex governance capabilities and aggregates metadata from within Google Cloud, external cloud applications, and third-party catalogues. It then enriches the metadata to map relationships across structured and unstructured data.

Gutmans described Knowledge Catalog as a “flywheel” built directly on top of existing access controls, ensuring agents cannot surface or act upon data they are not authorised to view.

Recognising the realities of heterogeneous IT environments, Google also introduced the Cross-Cloud Lakehouse. The offering allows enterprises to run Google’s BigQuery and AI capabilities against data residing in Amazon Web Services and Microsoft Azure, while also providing zero-copy integrations with enterprise systems like SAP and Workday.

Gutmans was keen to split hairs on the terminology: “It’s very important to note that the term ‘cross-cloud’ is distinct from ‘multi-cloud’; those who refer to multi-cloud are just talking about multiple single-cloud environments”.

Another significant update was Spanner Omni. Historically, Google Cloud Spanner, a globally distributed relational database, was tethered to Google’s infrastructure, including storage, as well as GPS receivers and atomic clocks to ensure transactional consistency.

Driven by enterprise demand for disconnected edge and on-premises deployments via Google Distributed Cloud, Google has engineered Spanner to run independently.

“Three, four years ago, no one, including us, believed we could disconnect Spanner from Google Cloud,” Gutmans said. Now, Spanner Omni offers vector processing, search, and graph capabilities for disconnected environments, which Gutmans noted is key for highly regulated use cases like on-premises fraud detection.