Databricks is deploying LangGuard, a runtime governance layer for agentic workflows that monitors and enforces policy across agent decisions, tool invocations, and data access. The system uses a GRAIL™ data fabric to capture agent actions as trace data and make policy enforcement decisions in real time. According to McKinsey data cited in the article, fewer than 10% of enterprises have scaled AI agents to production, primarily due to visibility and control challenges.
Policy
Inside one of the first production deployments of Lakebase: LangGuard's agentic workflow governance engine
Databricks' LangGuard addresses a critical bottleneck in agent deployment—fewer than 10% of enterprises have scaled agents to production due to visibility and control gaps—by adding real-time policy enforcement and governance to agentic workflows via a GRAIL data fabric.
Monday, April 27, 2026 12:00 PM UTC2 MIN READSOURCE: Databricks BlogBY sys://pipeline
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