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AI agent governance
Governanceagent governance
AI agent governance is the set of policies, controls and accountabilities that ensure autonomous agents act within defined limits: permissions, traceability (audit trail), human approvals, continuous evaluation and accountability. It answers who is accountable when an agent gets it wrong.
It goes beyond watching what an agent does (observability): it defines what it is allowed to do, with which permissions, and who bears responsibility. It includes permission matrices, guardrails, human approvals at risk points and full traceability. It is gaining weight as an investment and product category (agents that watch other agents).
How it differs from
- Agent observability
- Observability sees what the agent does; governance defines what it is allowed to do and who is accountable
- Compliance
- Compliance verifies adherence to rules; governance also includes designing internal controls and accountabilities
Examples
- A compliance agent that watches other agents' actions and leaves an audit trail
- A permission matrix that stops an agent from executing payments without human approval
FAQ
- Does the law require me to govern my agents?
- It depends on the use case and country. In the EU, the AI Act imposes obligations on high-risk systems (deadlines postponed to December 2027) and in Spain the AESIA has had sanctioning power since August 2026; but governance is good practice beyond any legal obligation.