AI agents can't just be turned off; you need a tested 'kill switch' strategy.

When an AI agent performing a routine task, like sorting support tickets, receives unusual instructions, simply pausing the model might not stop the action. The agent is part of a larger system: its identity holds credentials, an orchestrator runs workflows, and connected tools carry out the work. A queued job, an active session, or a separate service can continue to execute even after the central AI model is disabled.

For engineers and security teams, the practical impact is that stopping an AI agent requires a multi-layered containment plan. A true 'kill switch' needs to halt active and queued runs, revoke all associated credentials, disable tool access, and restrict network paths. The key takeaway is that security depends on the specific architecture; you must test your controls to understand what 'stop' actually means in your deployment.