The modern data stack was built for humans asking questions. Google just rebuilt its for agents taking action.

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 intervention. Cross-cloud lakehouse. Lets BigQuery query Iceberg tables on AWS S3 via private network with no egress fees. Data 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 action

The 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 that problem. It is an evolution of Dataplex, Google's existing data governance product, with a materially different architecture underneath. Where traditional data catalogs required data stewards to manually label tables, define business terms and build glossaries, the Knowledge Catalog automates that process using agents.

Google's lakehouse goes cross cloud

Google has had a data lakehouse called BigLake since 2022. The new approach is storage-based sharing via the open Apache Iceberg format. That means whether the data is in Amazon S3 or in Google Cloud, it doesn't make a difference.

The practical result is that BigQuery can query Iceberg tables sitting on Amazon S3 via Google's Cross-Cloud Interconnect, a dedicated private networking layer, with no egress fees and price-performance Google says is comparable to native AWS warehouses.

From writing pipelines to describing outcomes

The Data Agent Kit ships as a portable set of skills, MCP tools and IDE extensions. The architectural shift it enables is a move from what Gutmans called a "prescriptive copilot experience" to intent-driven engineering.

Rather than writing a Spark pipeline to move data from source A to destination B, a data engineer describes the outcome — a cleaned dataset ready for model training — and the agent selects whether to use BigQuery, the Lightning Engine for Apache Spark or Spanner to execute it, then generates production-ready code.

Where Google and its rivals diverge

The premise that agents require semantic context, not just data access, is shared across the market. Databricks has Unity Catalog, Snowflake has Cortex, and Microsoft Fabric includes a semantic model layer.

Google is positioning openness as a differentiator, with bidirectional federation into Databricks Unity Catalog and Snowflake Polaris via the open Iceberg REST Catalog standard, rather than requiring customers to start over.

What this means for enterprises

Google's argument is that enterprises are behind on three fronts: Semantic context is becoming infrastructure; cross-cloud egress costs are a hidden tax on agentic AI; and the pipeline-writing era is ending. Data engineers who move toward outcome-based orchestration now will have a significant head start.