# Databricks — Data-Native AI Agents: Why Agents Must Move to Your Data

- Company: Databricks (databricks.com)
- Announced: 2026-07-15T17:30:00+00:00
- Category: not stated
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.databricks.com/blog/data-native-ai-agents-why-agents-must-move-your-data
- Record: https://forck.live/items/17506-data-native-ai-agents-why-agents-must-move-to-your-data
- Subject: Mosaic AI

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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