Fabric Agentic Analytics and Lakehouse Schema Design

This article examines the impact of schema contracts, semantic models, and governed lakehouse design on Microsoft Fabric agentic analytics. It argues that better schema design reduces the need for AI agents to guess, improving safety and efficiency. The piece covers architecture patterns, production examples, failure modes, guardrails, and operational checklists for engineers, data owners, and executives. It references Microsoft Build 2026 announcements and emphasizes that clear schemas, catalog behavior, semantic definitions, and cost controls are critical for agentic analytics. The article provides practical guidance for rollout, metrics, and avoiding weak implementations, making it relevant for IT professionals working with Fabric, lakehouse architecture, and AI-driven analytics.