The engineering behind Genie Ontology - makes data agents work

​Most AI data assistants fail because they don't understand specific corporate context. If you ask a generic model to calculate a metric like active users, it usually guesses or pulls a random definition from an outdated table. The new Databricks Genie Ontology fixes this by acting as a self-improving context layer directly on top of Unity Catalog. Instead of relying on manual mapping, it continuously scans your queries, pipelines, dashboards, and connected apps to build a living knowledge graph of your company's unique business definitions. ​The most interesting part is how it resolves conflicting definitions using an approach called OntoRank (essentially Google's PageRank algorithm applied to enterprise metadata). It automatically ranks definitions based on creator authority, usage frequency, data freshness, and proximity to certified assets. This allows Genie One to consistently surface the exact trusted metrics the business teams expect, bypassing the classic text-to-SQL hallucination bottleneck without blowing up your token context. ​This video features an in-depth product overview and a technical walk-through showing exactly how Genie One leverages the Ontology framework to execute trustworthy workflows.