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To stop an AI agent from carrying out an unintended action, put a control before consequential tool calls: validate the proposed action, require human approval where the impact warrants it, and ensure an operator can deny it. Use live status and event traces to understand what is happening, then investigate external systems and reverse any completed effects through their supported processes. Monitoring can help detect problems, but it is not a substitute for a pre-action gate.
Know which controls can prevent an action
| Control | What it can do | Important limitation |
|---|---|---|
| Input, output, or tool guardrail | Automatically check a request, final output, or tool input or result. | It only checks the conditions it was designed to inspect. A check after a tool call cannot prevent that call’s side effect. |
| Human approval | Pause an approval-required tool action so a reviewer can approve or reject it before execution. | Requires application support and a timely reviewer. The pause and resume behavior varies by framework. |
| Trace and event inspection | Help reconstruct model generations, tool calls, handoffs, guardrails, and other recorded events. | Recording activity does not itself prevent an action, and a trace may not include every application event or downstream effect. |
| Asynchronous monitoring | Review activity for potential concerns and, in some systems, flag or stop a conversation. | It can miss problems or flag legitimate activity; do not make it the only safeguard. |
| Application or infrastructure stop control | Give an operator a way to request that further work stop. | There is no universal guarantee of immediate termination or rollback of a side effect that already occurred. |
OpenAI’s Agents SDK human-in-the-loop guide describes approval interruptions that can be resolved before a run resumes. That is a specific SDK behavior, not a promise about every agent framework. OpenAI’s guidance puts the roles plainly: “Use guardrails for automatic checks and human review for approval decisions. Together, they define when a run should continue, pause, or stop.” (OpenAI, Guardrails and human review.)
Set up safeguards in the order actions happen
1. Inventory tools and consequences
List the tools the agent can call, the credentials they use, the systems they can reach, and the effects those calls can cause. Distinguish read-only operations from writes, messages sent to other people, edits, deletions, purchases, and other consequential changes. This gives you a basis for deciding which actions need automatic validation, human approval, or both.
2. Limit permissions and scope
Give the agent only the tools and access required for its task. Where work is authorized and sensitive, keep boundaries around the relevant identity, project, filesystem, and network rather than relying only on prompts to constrain behavior. For a proposed action, reviewers should be able to assess its target, operation, arguments, identity, and whether it falls within the authorized scope.
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3. Validate before a tool runs
Check tool arguments against allowed targets and actions before execution. Require human approval for high-impact or ambiguous calls, such as sending an external communication or changing an important record. OpenAI’s Agents SDK supports tools configured to always require approval or to require it based on parsed arguments; in its documented flow, a pending action becomes an interruption that can be approved or rejected before the run resumes. Consult the documentation for the framework you actually use rather than assuming this behavior is universal.
4. Make the pending action visible
Show the operator the proposed action, target, relevant arguments, and current state—waiting for approval, executing, or completed. Provide a reachable way to deny a pending call and a separate control to request cancellation of further run activity. Confirm how the application handles work already in flight: a cancellation request should not be treated as proof that a tool call stopped.
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5. Record decisions and events
Record timestamps, run identifiers, tool identity, arguments, approval decisions, results, and errors according to your deployment’s data-handling rules. OpenAI’s Agents SDK tracing guide describes events including model generations, tool calls, handoffs, guardrails, and custom events. Tracing is unavailable to organizations using the APIs under Zero Data Retention policy. A trace can aid investigation, but do not assume it captures every application event or downstream effect.
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Before deployment, test denied approvals, malformed arguments, duplicate calls, monitor timeouts, unavailable tracing, and cases where an action completes while an operator is responding. Check what happens if an approval request is abandoned or the monitoring component fails; the safe behavior depends on your application’s design.
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What to do when an agent is acting unexpectedly
- Deny a pending action. If it has not executed and is awaiting approval, reject it through the application’s approval control.
- Request that the run stop. Use the application’s operator control to prevent further work. Check its documented semantics for in-flight calls; do not assume a stop request cancels work already handed to an external tool.
- Inspect the event record. Use the run trace and application logs to identify what the agent proposed and called, which approvals were granted or rejected, and what results were returned.
- Check the connected system directly. Verify whether the external action actually took effect. A trace is useful evidence, but it is not proof that every downstream event was recorded.
- Recover through the affected system. If a change completed, use that system’s supported correction, cancellation, or recovery process. Stopping the agent does not automatically undo completed actions.
Evaluate a monitoring or agent framework
Compare controls by where they operate and what they cover, not just by whether a product advertises “monitoring.” Ask:
- Does the check run before or after the side effect?
- Which tools, nested agents, and external actions are in scope?
- Can an action be paused, rejected, and resumed, and what happens on timeout?
- What can an operator see while a run is active?
- Which events are traced, and what data-retention restrictions apply?
- What happens if the monitor is unavailable or flags legitimate activity?
- Can the connected system reverse an action after it completes?
OpenAI’s misalignment monitoring guide warns that automated monitoring can miss issues or flag legitimate activity. Treat it as an additional signal, alongside controls in your application, rather than a safety guarantee. OpenAI also describes its own internal coding-agent monitoring approach in How we monitor internal coding agents for misalignment; that account is about OpenAI’s internal practice, not an independent effectiveness study or a general benchmark.
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