Enterprise data stacks were built for humans running scheduled queries. As AI agents increasingly act autonomously on behalf of businesses around the clock, that architecture is breaking down — and vendors are racing to rebuild it. Google’s answer, announced at Cloud Next on Wednesday, is the Agentic Data Cloud.The architecture has three pillars:Knowledge Catalog. Automates semantic metadata curation, inferring business logic from query logs without manual data steward interventionCross-cloud lakehouse. Lets BigQuery query Iceberg tables on AWS S3 via private network with no egress feesData Agent Kit. Drops MCP tools into VS Code, Claude Code and Gemini CLI so data engineers describe outcomes rather than write pipelines”The data architecture has to change now,” Andi Gutmans, VP and GM of Data Cloud at Google Cloud, told VentureBeat. “We’re moving from human scale to agent scale.”From system of intelligence to system of actionThe core premise behind Agentic Data Cloud is that enterprises are moving from human‑scale to agent‑scale operations.Historically, data platforms have been optimized for reporting, dashboarding, and some forecasting — what Google characterizes as “reactive intelligence.” In that model, humans interpret data and decide what to do.Now, with AI agents increasingly expected to take actions directly on behalf of the business, Gutmans argued that data platforms must evolve into systems of action.
“We need to make sure that all of enterprise data can be activated with AI, that includes both structured and unstructured data,” Gutmans said. “We need to make sure that there’s the right level of trust, which also means it’s not just about getting access to the data, but really understanding the data.”The Knowledge Catalog is Google’s answer to tha …