Operationalizing Genie Ontology for AI Agent Business Context on Databricks

Add table descriptions and column comments in Unity Catalog. Write for a new analyst who does not know your schema: say what the data represents, what its business purpose is, and flag any known caveats. Concentrate the effort where it pays off, on the curated, business-ready tables that dashboards and agents actually query

Add agent-facing metadata to your metrics. Metric Views can carry display names and synonyms, so natural language like "sales" maps to the right measure, format patterns for currency and dates, and example queries. This metadata flows into Genie Ontology, so the work you do to model a metric also makes it easier for an agent to find and use it correctly

Add fine-grained controls where the data requires them. For sensitive data, row-level security restricts which rows a user can see, and column masking obscures sensitive values. Both are enforced at query time. Attribute-based access control lets you drive these protections from governed tags, so a policy tied to a sensitivity tag applies everywhere that tag appears rather than being defined table by table. This provides a scalable way to enforce row and column-level protection consistently across a large estate

Build a semantic model using Metric Views. A Metric View is a Unity Catalog object that defines your measures (the aggregated numbers, like total revenue) and dimensions (the ways you slice them, like region or month) once, as governed code. Because the aggregation is resolved at query time rather than baked in, consumers who query the Metric View use the same governed definition. This is one of the most important steps for accuracy because it removes the ambiguity that causes agents to choose the wrong definition. Define your critical KPIs as Metric Views first: these are the numbers that abs

Build a deep, well-used asset base. A workspace with many well-documented, widely used dashboards, queries, and Genie Agents gives the ontology more material to learn from than a sparse one does. As those assets are used and improved, the signals available for context extraction become richer