AI governance names the rules, gates, and rulings by which an agentic system governs its own agents at runtime, so that an AI AGENT acts within bounds its AGENTIC OPERATOR set, and leaves a record of having done so.
AI agent governance is the discipline of controlling what agents can do, how they can do it, and under what conditions — while maintaining the autonomy that makes them useful.
(Lussier, 2026)1
The governing happens inside the system, while the work runs. A rule states what an agent may do; a gate checks an action at the moment of the call and refuses or warns; a ruling records a decision the operator made once so no agent has to relitigate it; a receipt shows what the agent actually did. The operator supplies intent and judgment; the apparatus carries them into every session without the operator present. Governance in this sense forms part of the system’s own operations, not a review that arrives after release.
Its working parts, by function:
- rules — the standing conduct an agent reads at boot
- gates — the runtime checks that fire on an action, blocking or advising
- rulings — operator decisions banked once and applied everywhere
- receipts — the record each action leaves for later audit
The public sense of the term, the policy field that governs AI from outside through law and frameworks such as the EU AI Act and the NIST AI Risk Management Framework, addresses the builders of systems; the sense here addresses the running system itself.
See also
Footnotes
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Lussier, E. (2026, June 5). What is AI agent governance? A framework for keeping agents in check. Airia. https://airia.com/blog/what-is-ai-agent-governance-a-framework-for-keeping-agents-in-check/ ↩
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