The original title is "Why Your AI Agents Need a Semantic Layer" which is 8 words. It's already short and specific. Let me check the rules:

Short summary

AI agents using text-to-SQL produce inconsistent, ungoverned results because they interpret business logic per query instead of referencing shared definitions. A semantic layer fixes this by placing a governed metrics layer between agents and the warehouse, ensuring consistent calculations, structural RBAC, and auditable provenance. The article outlines three production failure modes—inconsistent metrics, data leaks, and missing audit trails—and argues for an agent-native semantic layer with Git-versioned YAML definitions.

• •Text-to-SQL agents produce inconsistent numbers because they interpret business logic per query
• •A semantic layer enforces governed metrics, multi-tenant RBAC, and audit trails for AI agents
• •Metrics should be defined in YAML, versioned in Git, and reviewed via pull requests

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