Enterprises moving AI agents to production require more than just data access; they need data understanding. Google Cloud's Knowledge Catalog for Looker Core now indexes LookML semantic language, bridging this gap. AI agents need context, including table schemas, lineage, and governance, but also deterministic business semantics for metric calculations. A single tool cannot provide both broad organizational context and precise metric definitions. Knowledge Catalog offers broad enterprise context, while LookML provides deterministic mathematical semantics. This integration indexes LookML business logic into Knowledge Catalog for Gemini Enterprise and custom AI agents. Without explicit semantic rules, LLM agents can create conflicting metric definitions and generate incorrect SQL due to naive join fan-outs. Knowledge Catalog and Looker resolve this by modeling semantics once in LookML and accessing them across Google Cloud's AI ecosystem. This new release indexes LookML's deterministic semantics as a first-class layer in Knowledge Catalog, providing agents with exact definitions, join paths, and lineage. This metadata is automatically available to Gemini Enterprise without custom code, enabling it to generate accurate SQL by referencing technical schemas, glossaries, and indexed LookML logic. Looker Core on Google Cloud automatically indexes LookML models, Explores, Views, lineage, and dashboards directly into Knowledge Catalog. This integration enables agents to retrieve both broad context from Knowledge Catalog and explicit mathematical relationships from LookML.