How to govern AI agents, not just models
- AI agents act rather than just predict, which broadens their potential impact and changes the risk profile.
- The foundational control is permissions: tightly defining and constraining what an agent can do.
- Maintain meaningful human oversight and ensure action-level traceability so agents are not black boxes.
- Assign clear human accountability and keep agents within one coherent governance practice, not a separate track.
- Current as of June 2026. This is general information, not legal advice.
Why agents are different
A traditional AI model makes a prediction or generates content, and a human or system decides what to do with that output. An AI agent goes further: it can act, taking steps, using tools, and making changes in pursuit of a goal, sometimes across many steps with limited human intervention. This shift from advising to acting changes the risk profile. An agent's mistakes are not just wrong answers; they can be wrong actions with real consequences. Governance has to account for this.
Govern what the agent can do
The foundation of agent governance is permissions: defining and constraining what an agent is allowed to do. Just as you would not give a person unlimited access, an agent should operate within clear boundaries, what systems it can touch, what actions it can take, what limits apply. Governing the agent's permissions, and keeping them as tight as its purpose allows, is the single most important control, because it bounds the potential impact of anything going wrong.
Maintain meaningful human oversight
Agents can act quickly and autonomously, which makes human oversight both harder and more important. Good governance ensures there are meaningful points of human control: approvals for consequential actions, the ability to monitor what an agent is doing, and the ability to intervene or stop it. Oversight that cannot keep pace with the agent, or that exists only nominally, is a serious gap. The design should make real oversight possible.
Ensure traceability
Because an agent takes multiple actions, often across systems, traceability is essential. Governance should ensure an agent's actions are logged in enough detail to reconstruct what it did and why. This matters for detecting problems, for accountability, and for the after-the-fact review that any consequential action deserves. Without traceability, an agent is a black box that acts, which is the opposite of governable.
Assign clear accountability
Agents do not absorb accountability; people do. Governance must assign clear human accountability for each agent: who owns it, who is responsible for its actions, who decides its permissions and reviews its behaviour. The autonomy of an agent makes this more important, not less, because the chain from human decision to agent action must remain clear even when the agent operates with limited supervision.
Connect agents to your wider governance
Agents should sit within your overall AI governance, not in a separate track. They belong in your AI inventory, classified by risk, with controls, oversight, and evidence like any other AI system, plus the agent-specific concerns of permissions and action-level traceability. Keeping agents within one coherent governance practice, with the same connected record per system, is what lets you govern them alongside your models rather than treating agentic AI as an ungoverned exception. As agents become more capable and more common, governing them well becomes a defining test of an organisation's AI governance.
Key terms
- AI agent
- An AI system that takes actions to pursue goals, often across multiple steps and tools.
- Permissions
- The defined boundaries of what an agent is allowed to do.
- Traceability
- Logging of an agent's actions in enough detail to reconstruct what it did and why.
- Accountability
- Clear human responsibility for an agent and its actions.