AI-Ready Metadata Prevents Query Failures

This article discusses why most AI query failures are context failures rather than SQL failures, emphasizing the need for metadata to be actionable at query time. It covers architecture patterns with five layers (storage, catalog, execution, semantics, agent interface), common failure modes, guardrails for agentic use, and operational checklists for engineers, data owners, and executives. Grounded in standards like OpenLineage, Great Expectations, and Apache Iceberg, it provides practical guidance for data platform teams exposing analytical tools to LLMs, focusing on making metadata contracts explicit for both humans and software.