Owner
Name one person accountable for each agent and the work it is cleared to do.
Enterprise AI agent governance
Set the limits before the agent acts
Enterprise AI agent governance links each high-impact action to the person who approved it and a record of the result. Policy says what should happen. The work record shows what did happen.
A practical control model
Govern the agent’s actions
Model tests and safety rules matter. Your team also needs to know who approved the work, which limits applied, and what changed in the live system.
Approver
Name who may approve new work for the agent, and who may withdraw that approval.
Reviewer
Name who checks what the agent did, and how often.
Escalation contact
Name who is called first when something goes wrong.
Exceptions
Agree how unusual requests are handled and written down.
Review rhythm
Agree when approvals and past work are looked at again.
The review path
Give each team the same operating story
Operations can check the action and result. Security can see who was cleared to do what. Audit and risk teams can follow the evidence without piecing the event together from separate logs.
See the audit trail modelStart with one consequential action