What are the key components of an AI governance policy for agentic AI?

An agentic policy has to govern actions, not usage. Policies written for chat assistants ask what employees may put into a model. Agents invert the question: what can this thing do once it decides to do it.

Five components carry most of the weight.

• Approved use cases and tool permissions. Scope each agent to the tasks it was built for, and list the tools, services, and MCP servers it may call. An agent's risk comes from its reach, not from the model behind it.

• Data access boundaries. Write down what each agent may read, write, and act on. Access to the CRM is not a boundary. Read on contact records, no write, no export is.

• Human approval thresholds. Decide which classes of action require a person before execution: payments, deletions, external communication, privilege changes. Set the threshold by blast radius, not by likelihood.

• AI-specific incident response. An agent failure rarely looks like an outage. It looks like a sequence of individually reasonable actions that added up to something nobody sanctioned. Your runbook needs a path for that.

• Testing and audit cadence. Approval expires. Fix a re-test interval, and re-test after every model or tool change regardless of where that interval falls.

None of this enforces itself. A document has no mechanism for noticing when an agent finds a path its author did not anticipate, and agents do that routinely, because improvising toward a goal is the entire point of using one.

Akto Argus supplies the enforcement half through continuous red teaming and runtime monitoring, checking whether deployed agents stay inside the boundaries the policy describes.

Write the policy for what agents do when nobody is watching. That is the only condition they run in.