Built for Agents and Managed by Agents

This article analyzes the Dremio Agentic Lakehouse, distinguishing between two key aspects: data designed for agent access (governed SQL, semantic context) and platform work managed by agents (optimization, metadata, maintenance). It covers architecture patterns, production examples, failure modes, guardrails, and operational checklists for engineers, data owners, and executives. Grounded in Dremio MCP Server, Apache Iceberg, and Polaris documentation, it emphasizes explicit contracts for storage, catalog, identity, quality, and cost controls. The article provides practical guidance for realistic pilots, metrics, and rollout strategies, avoiding hype and focusing on production-ready capabilities for AI platform teams.