LookML metadata indexing in Knowledge Catalog is now GA | Google Cloud Blog
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Unifying enterprise context and semantics: LookML in Knowledge Catalog is now GA
October 27, 2026
By Maire Newton, Outbound Product Manager, Google Cloud
and Ramanan Balakrishnan, Group Outbound Product Manager, Data Cloud
As enterprises move from experimental AI agent prototypes to production use, data teams are realizing that access to data is not the same as understanding that data. To address this gap, we are excited to announce the general availability (GA) of LookML semantic language indexing in Knowledge Catalog for Looker Core on Google Cloud.
To answer complex business questions accurately, AI agents require context – table schemas, data lineage, governance policies, business definitions, etc. However, context is not a monolithic concept. Knowing where data lives is distinct from knowing how to calculate metrics with it. Enterprises often mistakenly combine broad organizational context (metadata from structured and unstructured sources) with deterministic business semantics (SQL formulas, join paths, and filtering rules for metrics like net revenue or churn).
In reality, no single tool can handle both organizational context and deterministic business semantics for agentic AI. Google Cloud environments provide these through Knowledge Catalog and LookML:
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Broad enterprise context with Knowledge Catalog: Agents need a unified context graph that aggregates metadata from structured tables and unstructured documents. Knowledge Catalog enables agents to discover assets, assess data quality, trace end-to-end lineage, respect privacy tags, and access corporate policy documents. This context graph informs an agent about existing data, governance, and connections.
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Deterministic mathematical semantics with LookML: LookML provides the mathematical formulas and business logic that define how data is transformed and analyzed.