Govern AI agents before granting autonomy

An agent that can act introduces a different exposure from a tool that only suggests. Map its available tools, identities, accessible data and possible actions before deciding how much autonomy to grant. Start with a bounded workflow and a measurable outcome. Avoid treating successful demonstrations as proof that unattended production use is appropriate.

Apply least-privilege access and separate recommendation from authorization. Restrict tools and data to the workflow’s needs; establish transaction and scope limits; require an authorized person to approve irreversible or consequential actions. Test that these limits are enforced by the surrounding system, rather than relying only on a prompt telling the agent to behave.

Evaluate normal operation, ambiguous instructions, malicious content, tool failures and attempts to exceed permissions. Specify when the agent should stop, request clarification or hand work to a person. Document who owns that escalation and how quickly it must be handled. A human in the loop is not an effective control if no one is available or able to judge the decision.

Roll out in stages: observe recommendations, run supervised actions, then expand only when performance and controls meet agreed criteria. Monitor tool use, exceptions, outcomes and material changes. Provide a tested way to revoke access, pause execution and recover the workflow if the agent behaves unexpectedly.

Review autonomy as an ongoing authorization, not a one-time approval. Reassess after changes to models, tools, data or business processes. Report incidents and near misses alongside business value. Leaders should know who can stop each agent and which evidence supports continued operation. This playbook is general guidance, not legal, regulatory or audit advice.

General executive guidance, not legal, regulatory or audit advice. Diagnostics are self-assessments, not certifications or independent assurance.