Theory background
The principal-agent problem appears when one party delegates work to another and cannot perfectly observe the agent's effort, judgment, or information. Governance is needed because delegation creates room for misalignment and hidden action.
In companies, this problem appears in sales incentives, management reporting, vendor relationships, and delegated approvals. AI agents add a new form of delegated action.
Translate to AI adoption
An AI agent may draft replies, retrieve data, update records, call tools, prepare reports, or recommend decisions. It acts for a user or organization, but it does not carry human responsibility.
The risk is not only technical error. The risk is unclear delegation: no one knows what the agent was allowed to do, what it actually did, or who was supposed to review the result.
Managerial implication
AI agent design should separate authority levels. Some actions can be automatic, some can be recommended, some require approval, and some should remain outside the agent's scope.
Monitoring should be built into the workflow. Logs, citations, tool-call records, exception queues, and review checkpoints are the practical equivalents of governance controls.
What to test
Before deployment, test whether the agent respects its authority boundary, whether users understand that boundary, and whether exceptions reach the correct human owner.
A trustworthy agent is not one that acts everywhere. It is one whose delegated role is narrow enough to monitor and useful enough to justify the delegation.