Databricks Unifies Data and Governance for Agentic AI with New Lakehouse Features
What happened
Azure Databricks unveiled significant platform expansions, including Agentic Data with LTAP Architecture and Lakebase, to provide a unified, governed architecture for transitioning experimental AI pilots to production agentic workloads. These new features aim to solve the decades-old data pipeline problem that has been slowing AI agents by offering millisecond query latency directly on governed data.
Why it matters
For AI Engineers and Data Engineers transitioning experimental AI pilots to production, Azure Databricks' new capabilities offer a unified, governed architecture, with Lakebase providing zero-copy database branching for safe agent debugging and Lakehouse//RT enabling real-time analytics on governed data.
Topics
- Azure Databricks
- Agentic AI
- Lakehouse Architecture
- Real-time Analytics
Articles in this trend
- Unifying Data and Governance in the Agentic Era: What’s New with Azure Databricks — Databricks
- Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents — VentureBeat
- The AGI moment? Databricks’ new releases zero in on support and deployment of AI agents — AI – SiliconANGLE
- Lakeflow: A new era of agentic data engineering — Databricks
- Introducing CustomerLake: The Agentic CDP embedded in Databricks — Databricks
- What’s new in Genie Code at Data + AI Summit 2026 — Databricks
- Databricks’ new agentic coworker Genie One brings AI automation to every part of the business — AI – SiliconANGLE
- Introducing Genie One, Genie Agents, and Genie Ontology — Databricks
- Introducing Genie ZeroOps: Put your data and AI operations on autopilot — Databricks