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

An effective agentic AI governance policy needs to address several components that go beyond what a policy for simpler, single turn AI systems would typically cover. It should define approved use cases and specific tool permissions for each deployed agent, since the risk profile of an agent depends heavily on what tools and systems it has been granted access to rather than just what model powers it. Data access boundaries need explicit definition, spelling out what categories of information a given agent is permitted to read, process, or act upon.

Human in the loop requirements matter particularly for higher risk actions, meaning the policy should specify which categories of agent actions require explicit human approval before execution rather than being carried out autonomously. Incident response procedures need to account for failure modes specific to AI systems, which often look different from traditional software incidents and may involve unexpected agent behavior rather than a clear system outage. Finally, the policy should establish a testing and audit cadence, specifying how frequently agents get re evaluated rather than assuming an initial approval remains valid indefinitely.

Because agents act autonomously across multiple steps and can encounter situations their designers did not explicitly anticipate, policy language alone cannot guarantee compliant behavior. It needs technical backing to actually hold, since static rules written into a document have no mechanism for catching behavioral deviation on their own. Akto's Argus applies continuous red teaming and runtime monitoring specifically to verify that agents actually stay within the boundaries a governance policy sets, closing the gap between documented policy and actual agent behavior.