AI-ready metadata reduces query failures by making ownership, freshness, lineage, quality, and policy visible at execution time.
How to run autonomous materialization for agent query workloads: tie it to workload evidence, add governance checks, measure results, and avoid failure modes.
Why a metric catalog is not enough for AI agents, and how composable semantic layers keep business logic intact across multi-step analysis, with a rollout plan.
Dremio Agentic Lakehouse is easiest to understand as two ideas: data built for agent access and platform work managed by agents.
