Unifying enterprise context and semantics: LookML in Knowledge Catalog is now GA

Google Cloud announced general availability of LookML semantic language indexing in Knowledge Catalog for Looker Core on Google Cloud, making LookML business logic automatically indexed into the catalog by default. The integration gives Gemini Enterprise and custom AI agents access to deterministic BI semantics — exact metric definitions, join paths, and lineage back to source tables — alongside enterprise-wide discovery, with live sync reflecting production LookML updates in near-real-time. Developers building custom agents can access the same context bundles through the LookupContext API.

As enterprises go from experimental AI agent prototypes to using them in production, data teams are realizing that access to data is not the same as understanding that data. To help bridge that gap, today, we are announcing the general availability GA of LookML semantic language indexing in Knowledge Catalog for Looker Core on Google Cloud https://cloud.google.com/looker . To answer a complex business question accurately, an AI agent needs context — table schemas, data lineage, governance policies, business definitions, etc.. However, context is not a monolith; knowing where data lives is very different from knowing how to calculate metrics with it. In the enterprise, teams often conflate broad organizational context usually in the form of metadata from structured and unstructured documents with deterministic business semantics the exact SQL formulas, join paths, and filtering rules that define metrics like net revenue or churn , assuming that a single data catalog or a business intelligence BI tool handling both enterprise-wide asset discovery and precise metric calculation. In reality, no one tool can carry the entire load for agentic AI, as enterprise agents require both organizational context and deterministic business semantics. In Google Cloud environments, those are provided by Knowledge Catalog and LookML: With this native integration, the LookML business logic that powers your trusted BI reporting is automatically indexed into Knowledge Catalog — giving Gemini Enterprise and custom AI agents both enterprise-wide discovery and deterministic BI semantics. When an LLM agent connects directly to raw database tables — even when armed with basic table descriptions or a business glossary — it still has to infer mathematical relationships on the fly. Without explicit semantic rules, agents routinely fall into two traps: Knowledge Catalog and Looker solve this together, enabling you to model once in LookML and access those semantics everywhere across Google Cloud's AI ecosystem. Knowledge Catalog enforces semantic math when that logic is defined in a semantic modeling tool; LookML provides a mature, production-proven language for defining those exact mathematical relationships. Previously, catalog integrations only captured shallow, file-level pointers and basic dashboard inventories. With this release, LookML's deterministic semantics become a first-class layer in the Knowledge Catalog context graph. An agent that finds a metric in the graph now also finds its exact definition, its join path, and its lineage back to source tables — in the same place, under the same governance. Synchronization is always on by default with no action required from end users, and with live sync, any update pushed to production LookML is reflected in Knowledge Catalog in near-real-time so agents operate on the freshest business logic. Because this metadata is indexed directly into Knowledge Catalog by default, it is immediately available to Gemini Enterprise https://docs.cloud.google.com/gemini/enterprise/docs/connectors/connect-knowledge-catalog configure-knowledge-catalog , without custom agent code or separate catalog data stores. When connected to Knowledge Catalog, Gemini Enterprise uses built-in read-only skills and hybrid search to look up technical schemas, business glossaries, and indexed LookML logic in a single step, passing that rich context directly to your federated BigQuery connector to generate accurate SQL. For developers building custom agents, the same context bundles are also accessible via the LookupContext API. Knowledge Catalog now captures and links the following entity levels across Looker Core on Google Cloud: Bringing LookML into Knowledge Catalog gives Gemini Enterprise and your broader agent ecosystem a single place to retrieve both broad organizational context and governance from Knowledge Catalog and explicit mathematical relationships and SQL definitions authored in LookML. To see the impact of combining these two layers in Gemini Enterprise, compare how three architectures handle a complex cross-domain prompt: "What was our total revenue last fiscal quarter by product category, and which regional promotional policy limits apply?" | Agent architecture | System mechanics | Data logic and governance resolution | User experience and token efficiency | | Pattern 1: Baseline database agent Data warehouse only | Direct LLM writing raw SQL against warehouse tables with no semantic or catalog context. | Ungoverned and non-deterministic Has no visibility into data quality, PII tags, or corporate documents, and guesses SQL metric logic blindly — assuming a standard January fiscal calendar, summing uncompleted orders, and exposing restricted customer PII. | Fragile and high-risk Prone to schema hallucinations, fails on questions requiring unstructured policy context, and burns tokens in multi-turn SQL debugging loops. | | Pattern 2: Context-grounded agent Knowledge Catalog without indexed semantics | Gemini Enterprise or another agent platform uses Knowledge Catalog to look up table descriptions, glossary terms, PII governance aspects, and unstructured policy PDFs, then writes SQL via the warehouse connector. | Context-grounded, but lacks semantic SQL logic Enforces Knowledge Catalog governance and retrieves unstructured promotional policy rules, but lacks semantic definitions for SQL metrics and table relationships. This approach can miss conditional accounting rules like order status = 'Complete' or cause join fan-outs across multi-grain tables. | Governed discovery, inconsistent math Solves asset discovery, compliance, and unstructured context, but still suffers from text-to-SQL calculation drift and multi-turn SQL retries. | | Pattern 3: Context + semantic-grounded agent Knowledge Catalog + Looker Core on Google Cloud with indexed LookML | Gemini Enterprise or another agent platform connects to Knowledge Catalog enriched with indexed LookML metadata, automatically retrieving both broad catalog context and LookML join paths and field formulas to ground its SQL generation. | Governed context plus semantic SQL definitions Enforces Knowledge Catalog PII aspects, retrieves unstructured policy PDFs, and grounds its SQL in LookML's exact mathematical definitions indexed in Knowledge Catalog, applying status = 'Complete', the May–July fiscal calendar, and predefined join conditions across warehouse tables. | Accurate and token-efficient: Pre-mapped LookML join blueprints and compiled SQL expressions replace guessing, prevent join fan-outs, reduce SQL retry loops, and let the agent point users to relevant Looker dashboards and Explores. | When you follow pattern 3 and pair Knowledge Catalog with Looker's semantic layer in an enterprise agent workflow, the agent moves from guessing at raw SQL to a governed, two-stage execution flow: Alongside the answer, the agent returns an interactive Open in Looker Explore link with the exact measures and filters pre-populated, letting users slice, dice, and inspect row-level line items in a single click. A common question from data leaders is: "If we use Looker Conversational Analytics, when do we use an enterprise agent grounded in Knowledge Catalog?" The two experiences are designed for complementary jobs across your organization: Because Looker Core on Google Cloud, Knowledge Catalog, and Gemini Enterprise are natively integrated, you can connect your existing LookML business logic to your enterprise AI assistant in just a few clicks, with no custom agent code required. To start discovering Looker semantics and grounding your agents: To dive deeper, read the Manage Looker Core on Google Cloud with Knowledge Catalog documentation https://docs.cloud.google.com/knowledge-catalog/docs/manage-looker-core-metadata and learn how to Connect to Knowledge Catalog in Gemini Enterprise https://docs.cloud.google.com/gemini/enterprise/docs/connectors/connect-knowledge-catalog configure-knowledge-catalog . To build a more customized experience, explore our hands-on ADK agent codelab on GitHub https://github.com/haengeunc/kc-looker-codelab .