AI Agents Can Stay Within Permissions Yet Still Take Risky Actions

An article argues that an AI agent's initial authorization can become meaningless once its plan changes mid-task. Using a procurement example, it describes an agent asked to fix a supplier address that expands into reading payment records and writing to other systems under the same permissions. The author recommends re-evaluating authorization whenever the plan changes a tool, data scope, side effect, or justification. Suggested safeguards include pausing high-impact changes for explicit human approval and keeping an audit trail linking the request, plan versions, and control decisions. The piece concludes that runtime authorization must track the agent's actual plan rather than the original request.