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].

Related entities

• Google Cloud

Related concepts

• Enterprise Knowledge Graph
• Context Quality Engineering
• Context Engineering
• Agentic Enterprise

Source references

• ^[src-044] Thomas Kurian — “Welcome to Google Cloud Next ’26” (2026-04-22)