Topic starter
22/06/2026 10:03 am
TL;DR: Enterprise context, policy guardrails, lineage, and runtime trust signals can follow data and agents into production as Collibra and Databricks expanded bi-directional governance across Unity Catalog, Genie, and Agent Bricks, according to Collibra. That matters because agentic AI fails quickly when metadata, access boundaries, and accountability are fragmented across platforms.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “Collibra Named Databricksâ Governance Partner of the Year; Collibra and Databricks Deepening a Partnership to Ground Agentic AI in Governed Context”.
Key questions
Q: How should security teams secure AI agents and integrations in Databricks environments?
A: Security teams should treat AI agents and integrations as active access paths, not passive connections.
Q: Why do autonomous agents create more risk when metadata and lineage are fragmented?
A: Because the agent can still act on authorised access while making decisions from stale or incomplete business context.
Q: What are the signs that agentic AI is drifting away from the rules it was designed to follow?
A: The clearest signs are stale outputs, inconsistent decisions, and behavior that no longer matches the workflow the agent was built to execute.
Practitioner guidance
- Map governed context to runtime decisions Identify where certified definitions, ownership, and quality signals need to be available at the exact point an AI agent consumes data or selects a tool.
- Tie agent approval to live evidence Require runtime trust signals such as pass rates, assessment status, and unmonitored-agent detection before allowing autonomous workflows to expand.
- Audit stale meaning in agent workflows Review where business definitions or lineage can drift away from current data usage, especially when natural-language interfaces are used for analytics and actions.
Bottom line: Agentic AI governance breaks down quickly when business context, lineage, and policy controls are not available at the point of execution.
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22/06/2026 10:47 am
Agentic AI governance fails when business context and runtime execution are separated. Collibra and Databricks are responding to a control problem that spans data governance and identity governance at once: agents need certified meaning at the moment of action, not after the fact. When the context layer is disconnected from the execution layer, policy becomes descriptive instead of enforceable. Practitioners should treat governed context as part of the control surface, not just reference data.
A few things that frame the scale:
- 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
A question worth separating out:
Q: How do data governance controls differ from agent authorisation controls?
A: Data governance defines meaning, ownership, quality, and policy context for the data estate, while agent authorisation governs what an AI system may do with that context at runtime. In agentic environments, both must work together or the agent can remain technically authorised but operationally unsafe.
👉 Read our full editorial: Collibra and Databricks extend governance into agentic AI operations