Agentic Data Cloud

Google Cloud's AI-native data architecture for moving enterprises from reactive "systems of intelligence" to proactive "systems of action" where agents can perceive, reason, and act on trusted business data in real time.

Key points

  • The keynote argues that agentic AI requires data architecture built for agent scale, not human-scale dashboards and archives [src-044].
  • Cross-Cloud Lakehouse standardizes on Apache Iceberg and aims to query data in AWS or Azure without data movement or vendor lock-in [src-044].
  • Lightning Engine for Apache Spark is positioned as a faster serverless engine for large datasets [src-044].
  • Data Agent Kit gives data practitioners Gemini-powered intent-driven development across Python, Spark, and SQL [src-044].
  • Knowledge Catalog creates a dynamic context graph of enterprise data and semantics so agents can be grounded in trusted business context [src-044].
  • Deep Research Agent connects Gemini Enterprise research workflows to structured platforms such as BigQuery for cited enterprise analysis [src-044].
  • [src-097] adds concrete Data Agents across Conversational Analytics, Data Engineering Agent, Data Science Agent, Database Observability Agent, and Database Onboarding Agent.
  • [src-100] adds Open Knowledge Format as a portable knowledge specification that fits the Agentic Data Cloud thesis: agents need structured, inspectable business context rather than isolated prompt text.

Related entities

Related concepts

Source references

  • [src-044] Thomas Kurian — "Welcome to Google Cloud Next '26" (2026-04-22)
  • [src-097] Sean Rhee and Geeta Banda – "New data agents across the Agentic Data Cloud" (2026-06-16)
  • [src-100] Sam McVeety and Amir Hormati – "How the Open Knowledge Format can improve data sharing" (2026-06-12)

2026-07-01 BigQuery and Spanner expansion

  • Google Cloud's July batch strengthens Agentic Data Cloud infrastructure: Spanner is framed as a multi-model context engine, BigQuery AI.AGG brings large-scale synthesis into SQL, Conversational Analytics reaches GA, and BigQuery Graph is shown in fraud-network analysis [src-197][src-198][src-199][src-200].

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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