Lakehouse as the Operating Layer for Agentic AI

This article discusses the evolution of the lakehouse from a passive storage pattern to an active operating layer for agentic AI systems. It emphasizes that while AI models generate queries, the lakehouse determines query validity, permissions, performance, and auditability. The content covers architecture patterns, production examples, failure modes, guardrails, and operational checklists for engineers, data owners, and executives. It references research from Dremio Agentic Lakehouse and Trustworthy AI papers, advocating for governed data access, semantic context, and cost-aware execution. The article provides practical guidance for rolling out agentic analytics in production environments.