Picture the scene: Before an executive business review, your VP of Sales asks an AI assistant, "What was net revenue for our top enterprise accounts last quarter?" In seconds, the agent returns a polished chart! Unfortunately, the numbers don't match the finance report. Why? Because the agent counted cancelled orders as revenue and pulled from an unvalidated staging table.
The agent didn't fail because of the model, or because it couldn't translate a user question into a query. It failed because it didn't understand your business. The real kicker is that understanding usually already exists in the form of a semantic model in a Looker, dbt, Snowflake, Databricks, or SAP system. But because each definition stays locked inside its own tool, every agent guesses at it… and often gets it wrong.
This is where Knowledge Catalog comes in. It brings the semantic models from all of those tools into one context graph, so every agent works from the same trusted definitions. And when two tools define the same metric differently, both definitions appear with their provenance, so data stewards can reconcile the conflict before it reaches end users.
Today, we're announcing the general availability of expanded semantic model support. Knowledge Catalog now natively ingests semantic models and metric definitions from Looker LookML, BigQuery Graph (including BigQuery measures), dbt MetricFlow, Snowflake Semantic Views, Databricks Metric Views, and SAP Core Schema Notation (CSN), and connects them with schema, lineage, data profiles, business glossary and other key metadata in a single context graph, without moving data or rewriting business logic in a new language.
By grounding your AI agents in those models, they get the same business definitions your analysts rely on, so their answers line up with the numbers your business trusts. In fact, based on internal benchmarks, grounding agents in Knowledge Catalog makes them nearly 4X more accurate, with up to 33% lower latency, and using up to 75% fewer tokens.
The unified context graph enables you to:
Reuse the semantic models you already trust: Knowledge Catalog grounds Gemini agent, BigQuery Conversational Analytics, and your custom agents in the metrics, filters, and join paths your teams have already defined, wherever they live.
Go beyond hand-curated definitions with self-learned context: Knowledge Catalog pairs the well-defined metrics and KPI definitions in your semantic models with relevant context it extracts from unstructured knowledge sources, such as operational playbooks, meeting notes, chat threads, code, and more. Powered by Gemini, self-learning enrichment continuously finds missing or outdated context and turns it into governed, reusable context sets, ensuring your agents' context stays current as definitions change, without relying only on manual curation.
Pair business logic with enterprise trust: Even the right formula produces the wrong answer when it runs on stale or uncertified data. That's why Knowledge Catalog can connect semantic definition to data quality scores, freshness signals, and lineage, so an agent knows what it can trust before it writes a line of SQL.
The context graph that Knowledge Catalog creates is a connected map of your data and semantic estate: tables, semantic models, glossary terms, and data products are nodes; lineage, associations, schema keys, joins mined from real query history, and the join paths declared in your semantic models are the edges. Ingested semantics land in that same graph, next to pre-existing governance and usage signals, so an agent moves from business concept to trusted formula and verified join paths in one traversal. Each source adds its own perspective on your business:
Looker LookML (GA): Indexes models, explores, views, and dashboards using Live sync from Looker Core, capturing compiled metric SQL, fan-out-safe join conditions, mandatory filters, and lineage from source tables to executive dashboards.
BigQuery Graph (GA): Indexes native property-graph definitions, node and edge tables, graph measures, and business synonyms, so agents reason over connected entities with Graph Query Language (GQL) traversals instead of brittle multi-table self-joins.
dbt MetricFlow (GA): Links source objects to dbt models and MetricFlow metrics, alongside dbt data quality test results, filters, saved queries, freshness SLAs, and downstream exposures.
SAP Core Schema Notation (CSN) via SAP Business Data Cloud (BDC) (GA): Captures business labels, dimension and cube modeling patterns, default aggregations, currency and unit bindings, key and composite join associations, and data sensitivity flags when SAP BDC Data Products are federated through Google Cloud Lakehouse.
Because every source lands as a node or an edge in the same graph, a LookML join, a dbt exposure, a Snowflake relationship, or an SAP association are traversable side by side, with links tying each one back to the physical tables it depends on. And nothing is silently overwritten: overlapping definitions stay side by side, each labeled with its source, giving data stewards the full picture they need to settle on a single version.
You can explore and govern all of this in Knowledge Studio (preview), your command center to explore and manage your context. Explore your context graph, including your semantic definitions, using natural language prompts and traverse connection paths using the context graph canvas. Then, once you've validated the right assets and definitions, you can package them into reusable context sets that map to specific use cases and domains in your organization, which Gemini and custom agents can consume in production.
Let’s get back to that net revenue question. With Knowledge Catalog, the VP of Sales’ agent issues a single context query to discover the trusted asset, applying the exact metric formula and filters that your domain experts defined, all while respecting join keys across systems, and confirming that the underlying data passed its quality checks. Better yet, the numbers in the report are correct!
To get started, explore your context graph in Knowledge Studio and visit the Knowledge Catalog documentation to connect to more sources.