What is AI agent governance?

AI agent governance comprises the rules, roles, and control mechanisms with which an organization determines which AI agent may do what. It includes a defined mandate per agent, access rights and approval limits, a unique agent identity, logging of all actions, and regular review of whether agents stay within those boundaries. It carries proven principles of IT and process governance over to actors that make decisions themselves.

Why does this matter?

Every agent that acts in systems creates a new point where things can go wrong – and a new question of who is accountable. Without governance, agents remain pilots, because audit, data protection, and business owners will not grant them real authority. On the regulatory side, the EU AI Act requires documentation, logging, and human oversight depending on the risk class.

What does this look like in practice?

Before a procurement agent goes live, a profile is created: goal, accountable person, permitted systems, a posting limit of €2,500, mandatory approval for new suppliers, logging of every action. Each quarter the logs are checked against that profile — to see whether the agent kept to it and whether the limits still fit.

Not to be confused with

Not the same as general AI governance, which regulates the use of AI models overall, such as data provenance and fairness. Agent governance comes on top because agents act rather than merely produce output. Nor the same as guardrails, which are a technical means within governance, not governance itself.