AI Agent Governance: Frequently Asked Questions
aigovernance
Straight answers to the questions leaders ask when they're deciding whether — and how — to let AI agents act inside real workflows.
When executives evaluate AI agents, the same questions surface across industries. Here are direct answers.
What's the difference between an AI chatbot and an AI agent?
A chatbot answers questions. An agent takes actions — it sends, books, buys, updates records, or triggers workflows. The governance bar is higher because the consequences are real.
Who is accountable when an AI agent makes a mistake?
A named human owner. Accountability cannot be delegated to the model. The owner defines scope, approves expansions, and is answerable for outcomes.
How do we prevent an agent from taking harmful actions?
Through bounded permissions, approval workflows for higher-risk actions, and monitoring. The agent should be technically incapable of exceeding its scope — not merely instructed not to.
What should be logged?
Every input, tool call, output, and decision, with timestamps and the data sources used. Logs must be reviewable by people who did not build the agent.
How do we know if an agent is trustworthy enough to widen its scope?
Track accuracy, exception rate, reversal rate, and human-override rate on its current scope. A clean record over a meaningful sample earns more autonomy. Guess and you'll pay for it.
Does responsible AI slow us down?
Unbounded deployment feels faster until the first incident. Governed rollout compounds; incidents don't.
Where does this fit in our broader AI strategy?
Agent governance is one layer of a full operating model — see our Responsible AI-by-Design Framework and Enterprise AI Adoption FAQ for the wider picture.