Databricks AI/BI

Connect Databricks AI/BI to the full platform so agent answers are traceable, semantic definitions stay accurate, and new questions can be modeled faster.

Agentic Analytics

Why Databricks AI/BI matters

Databricks AI/BI lets business users ask natural-language questions against lakehouse data. But answer quality is bounded by the accuracy of the semantic definitions it works from. Stale Unity Catalog definitions or underlying models produce wrong answers. The real-world platform around Databricks is rarely Databricks-only, and AI/BI has no way to trace whether an answer is wrong because of a stale definition, a source system change, or a transformation issue upstream.

Typedef connects Databricks AI/BI to the full platform graph, so answers are traceable across every system, drift is caught before it reaches agents, and data teams can model new business questions faster.

What Typedef unlocks

Explainable agent answers

Trace any Databricks AI/BI answer back through definitions, lakehouse tables, transformations, and the source systems that originated the data.

Semantic accuracy for agents

Surface when upstream changes have made the definitions AI/BI relies on stale, whether the change originated inside or outside Databricks.

Faster semantic expansion

Model new metrics and dimensions without manually tracing data through source systems, ingestion, and transformations.

Agent-to-dashboard reconciliation

Show where AI/BI answers diverge from dashboards or other agents and trace both paths to the exact point of divergence.

Related integrations

Trace any Cortex answer back through the semantic layer, warehouse, and source systems so users can verify why the agent produced a specific result.

Trace any WisdomAI answer through the Context Layer, the warehouse, and the upstream pipeline that built the data, so users can verify results end to end.