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

2026-04-22 · Source: VentureBeat · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

Google Cloud has introduced the Agentic Data Cloud, a new data architecture designed to support autonomous AI agents operating at "agent scale" rather than traditional human-scale operations. Announced at Cloud Next, this architecture addresses the limitations of existing enterprise data stacks, which were built for human-driven queries and reactive intelligence. The Agentic Data Cloud features three core pillars: a Knowledge Catalog for automated semantic metadata curation, a cross-cloud lakehouse enabling BigQuery to query Iceberg tables on AWS S3 without egress fees, and a Data Agent Kit that allows data engineers to describe desired outcomes instead of manually writing data pipelines. This shift aims to transform data platforms from systems of intelligence into systems of action, ensuring all enterprise data can be activated by AI with appropriate trust and understanding.

Key takeaway

For CTOs and VPs of Data evaluating their enterprise data strategy, Google's Agentic Data Cloud signals a critical shift towards agent-centric architectures. Your current manually curated data catalogs and proprietary cross-cloud data access methods will likely become bottlenecks for scaling AI agent workloads. Prioritize adopting automated semantic context solutions and open-standard, storage-based cross-cloud federation to avoid escalating egress costs and enable outcome-driven data engineering.

Key insights

Google's Agentic Data Cloud shifts data architecture from human-scale reporting to autonomous AI agent action.

Principles

• Automate semantic metadata curation.
• Enable cross-cloud data federation.
• Transition to outcome-driven data engineering.

Method

The Agentic Data Cloud uses a Knowledge Catalog for automated semantic context, a cross-cloud lakehouse for data access, and a Data Agent Kit for intent-driven pipeline generation.

In practice

• Implement automated data cataloging.
• Utilize open standards for cross-cloud data.
• Adopt outcome-based data pipeline orchestration.

Topics

• Agentic Data Cloud
• Knowledge Catalog
• Cross-cloud Lakehouse
• Data Agent Kit
• Apache Iceberg

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Engineer, AI Engineer, AI Architect

Related on AIssential

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• Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents — Databricks unifies transactional and analytical data at the storage layer, enabling millisecond latency for AI agents.
• The TechBeat: The IDE Is No Longer Just a Place to Write Code (6/17/2026) — AI is deeply integrating into development, operations, and business strategies, demanding new approaches.
• Google’s Cloud Next AI Highlights — Google's multi-layered AI strategy integrates hardware, compute hosting, and agent layers for a full-stack advantage.

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Editorial summary, takeaway, and curation by AIssential. Original article published by VentureBeat.