LookML metadata indexing in Knowledge Catalog is now GA

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.

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:

• Broad enterprise context with Knowledge Catalog: Agents need a unified context graph that aggregates metadata across structured tables and unstructured documents. Through Knowledge Catalog, agents discover assets, inspect data quality, trace end-to-end lineage, respect column-level privacy tags, and surface corporate policy documents. The context graph tells an agent what data exists, how it is governed, and how it is connected.
• Deterministic mathematical semantics with LookML: What a catalog does not author on its own is how the numbers are computed. Describing a table is not the same as defining how tables join or how metrics are calculated. Looker’s modeling language, LookML, encodes production-tested semantic logic — multi-grain join protections, conditional filters, and exact KPI formulas — so agents don’t guess SQL math at runtime.

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.

Why a context graph needs deterministic 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:

1. Metric definition drift: Standard text-to-SQL agents can invent conflicting formulas for foundational KPIs like net revenue or customer acquisition cost, failing to apply domain-specific accounting rules (such as recognizing revenue only when order_status=”Complete” or prorating annual contracts across active service days).
2. Naive join fan-outs: Complex schemas often contain one-to-many (1:N) and zero-to-many (0:N) relationships across invoices, line items, and refunds. Without pre-modeled join relationships and symmetric aggregation rules, agent-generated SQL can multiply rows and inflate financial totals.

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.

What’s new: LookML in Knowledge Catalog

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, 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:

• LookML models (looker-model): Captures descriptive metadata, database connection routing, include hierarchies, data validation tests (assert), and parent-child EntryLinks mapping models to their associated Explores, Views, and LookML dashboards.
• LookML Explores (looker-explore): Indexes full relational join logic (type, sql_on, relationship cardinality such as many_to_one fan-out protection, foreign_key, required_joins, and sql_where) as Schema-Join relationships. It also captures mandatory filter guardrails (always_filter, access_filter), inherited dimensions and measures, and predefined starter queries that help agents and analysts overcome the cold-start problem.
• LookML Views and fields (looker-view): Extracts column-level compiled LookML SQL expressions (for example, ${total_revenue} / NULLIF(${count}, 0)), primary keys, dimension group timeframes, field synonyms, and derived table transformation logic (including SQL and native derived tables, partition keys, and cluster keys).
• Dashboards, Looks, and tiles (looker-dashboard, looker-dashboard-element, looker-look): Catalogs both UI-created and LookML-defined dashboards and visualization tiles, complete with dashboard filters, chart types, ownership, and usage statistics. Users in Knowledge Catalog are able to see table- and column-level lineage from underlying BigQuery tables all the way to individual dashboard tiles, allowing data engineers to evaluate schema changes proactively, before disruptions occur.