Five-Layer Agentic Lakehouse Architecture

This article presents a five-layer agentic lakehouse architecture designed to prevent AI agents from directly querying raw storage, which can lead to semantic ambiguity, security risks, and audit gaps. The layers are: Data, Knowledge, Agent, Tool, and Policy. Each layer has a distinct role—Data provides access to storage, Knowledge supplies business context and meaning, Agents coordinate reasoning, Tools execute predefined actions, and Policy enforces governance and access control. The article details how these layers interact on a single request, offers a checklist for evaluating existing architectures, and discusses where Dremio fits. It targets IT professionals building trustworthy, auditable analytics systems with AI agents.