From the source
External agents break at scale: When AI agents run in a separate stack, enterprises face compounding penalties: fragmented governance, rising egress costs, sluggish multi-hop latency, and observability gaps that make production deployment risky.
Governance cannot be retrofitted: Post-hoc controls fail because agents compute over data rather than just retrieve it.
A financial summary shaped by ungoverned rows cannot be redacted after the fact.
Policy must be enforced at query planning time, and only data-native agents embed governance directly into computation.
Data-native agents on Databricks: By running agents within the Data + AI Platform, teams get Unity Catalog governance, AI Search retrieval, MLflow tracing, Lakebase state management, and AI Gateway traffic control as a single integrated stack, enabling them to ship trusted AI features faster with security and lineage built in.
Most enterprise AI pilots clear the same low bar: connect an LLM to your data, drop in a vector database, demo it to leadership.
The hard part shows up later.
Security flags the governance holes.
Latency in multi-step agents kills the user experience.
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