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An AI agent needs an emergency stop that is enforced outside the model. The safest design combines several controls: stop accepting new work, cancel the active run, reject every subsequent tool call, revoke or narrow credentials, and stop or isolate the worker and any physical equipment. These controls limit what happens next; they do not automatically undo a transaction, message, deletion, or machine movement that has already completed.
What an emergency stop must do
A conversational instruction such as “stop now” is not an independently enforced safety mechanism. A tool-using agent may be able to execute code, call APIs, change records, control a browser, send messages, or operate physical equipment. The application, tool executor, identity system, infrastructure controller, and—where relevant—the machine itself must enforce the stop.
NIST’s 2025 tool-use guidance groups agent capabilities by functionality, access pattern, risk, reliability, and modality. Its examples include authentication, computer use, code execution, software extensions, physical extensions, human interaction, and agent interaction. It also distinguishes read-only, constrained-write, and write access. Your stop path should therefore be mapped to every action surface rather than attached only to the chat interface.
Stopping future work is not rollback
A stop can prevent a pending tool call from starting, but it cannot reliably retract an email already sent, a payment already submitted, data already deleted, or a physical action already performed. Design compensation and reconciliation procedures separately. For an external operation, record its request ID, provider response, idempotency key, and final status so an operator can determine whether a safe reversal is possible.
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Approval and emergency termination are different
Human approval is a preventive checkpoint. It pauses a selected sensitive action until a person approves or rejects it. An emergency stop is a denial and cancellation path used when continuation itself is unsafe. Approval does not necessarily stop compute, revoke credentials, terminate other processes, or address actions already in flight.
The layered stop architecture
Use an authoritative stop state outside the agent’s context. A durable record such as RUN_STOPPED, associated with the run, tenant, and worker, should be readable by every component that can initiate work. A monotonic state transition prevents a late “resume” or retry message from silently clearing an emergency stop.
| Layer | Primary enforcement point | What it stops | Pause or termination | Recovery evidence |
|---|---|---|---|---|
| Intake gate | API, queue, or orchestrator | New instructions and retries for the affected run | Denial of new work | Rejected request IDs and queue disposition |
| Run cancellation | Run controller or orchestrator | The current agent loop and scheduled continuation | Cancellation or pause | Run state, cancellation time, and reason |
| Tool gate | Tool executor or gateway | New tool calls after the stop state is observed | Denial | Tool name, arguments hash, decision, and policy version |
| Credential control | Identity, secrets, and authorization services | API, database, filesystem, or browser permissions | Revocation or restriction | Credential version and revocation event |
| Worker isolation | Container, VM, sandbox, or host controller | Processes, network connections, and local execution | Pause, isolate, or terminate | Process and environment termination results |
| Physical stop | Machine-level safety system | Actuation of robots or machinery | Hardware emergency stop | Machine safety event and inspection record |
Step 1: Stop intake and quarantine queued work
- Mark the run stopped in an authoritative store. Write the state with an operator identity, timestamp, reason, and incident ID. Make the transition durable before returning success to the operator.
- Close the affected intake path. Reject new requests, scheduled retries, and callbacks for that run. If a queue is shared, quarantine only messages that can be identified safely; do not indiscriminately delete unrelated tenants’ work.
- Define queue semantics. Document whether rejected messages are canceled, retained for forensic review, or moved to a dead-letter queue. Include the run ID and stop event in each disposition.
- Prevent re-enqueue loops. Consumers should check the stop state before acknowledging, retrying, or publishing a follow-up task. A retry policy must not turn an emergency stop into repeated work.
Stopping intake is necessary but insufficient. A worker that already received an instruction can continue unless the run controller and tool executor enforce the same state.
Step 2: Cancel or pause the active run
- Signal the runner. Use your orchestrator’s cancellation mechanism or an internal cancellation token. Treat a natural-language message as a user-facing explanation, not as the enforcement signal.
- Make cancellation observable. The runner should publish a terminal or paused state, the cancellation reason, and the last completed step. Do not report “stopped” while a worker is still allowed to issue tools.
- Handle cooperative and forced cancellation. First allow a short, bounded cleanup period for the runner to close files and record outcomes. If it does not exit, escalate to process termination or environment isolation through the infrastructure controller.
- Invalidate stale continuations. Attach a generation or lease value to the run. Any delayed model response, callback, or retry carrying an older generation must be rejected.
Cancellation behavior varies by provider and runtime. Verify whether cancellation interrupts model generation, queued tool calls, streaming responses, and callbacks. Do not claim that a provider’s “cancel” operation reverses external side effects unless its documentation explicitly guarantees that behavior.
Step 3: Put a deny-by-default gate before every tool call
The tool executor is the last software boundary before an action reaches an external system. It must check the stop state immediately before dispatch, not only when the run starts.
- Load current authorization. Read the run state, tenant policy, credential status, and tool-specific permission from a strongly consistent source where practical.
- Check the stop condition. If the run is stopped, expired, quarantined, or missing its lease, return a structured denial and do not invoke the tool.
- Check the capability. Distinguish read-only, constrained-write, and write operations. A stop policy may allow evidence collection while denying mutations, but that exception must be explicit and separately authorized.
- Bind authorization to the exact call. Include the tool name, target resource, argument hash, run generation, and credential version in the decision record.
- Recheck around long operations. For streaming uploads, browser sessions, shell commands, and multi-step API operations, check cancellation between sub-steps and enforce execution timeouts.
- Return a safe result. Tell the agent that the call was denied because the run is stopped. Do not expose secrets or imply that an action succeeded.
Code execution deserves special treatment. Restrict filesystem paths, network destinations, subprocess creation, and resource limits independently of the model’s instructions. A tool that is broadly capable in normal operation should still have a constrained emergency profile, such as read-only diagnostics with no outbound network.
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Example decision flow
request = receive_tool_call(run_id, tool, arguments)
state = load_authoritative_state(run_id)
if state.status != "RUNNING":
record_denial(run_id, tool, "emergency_stop", state.version)
return deny("run stopped")
if credential_is_revoked(run_id) or not policy_allows(run_id, tool, arguments):
record_denial(run_id, tool, "authorization", state.version)
return deny("not authorized")
lease = acquire_short_execution_lease(run_id, state.version)
if lease.invalidated:
record_denial(run_id, tool, "lease_invalidated", state.version)
return deny("run stopped")
return dispatch_with_timeout(tool, arguments, lease)
This pattern is illustrative rather than a universal SDK. The critical property is that the gate is outside the model and executes for every action.
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Step 4: Revoke or narrow credentials
Stopping a run does not stop another process that already possesses a valid token. Use short-lived, audience-restricted credentials where possible, and maintain a rapid revocation path for long-lived secrets.
- Issue separate identities for each run, tenant, and high-risk tool.
- Grant the minimum resource and operation scope needed for the current step.
- Remove write permissions first when an incident involves data mutation.
- Rotate or revoke tokens that may have been copied into a worker, browser session, log, or subprocess.
- Disable unattended refresh and webhook credentials associated with the stopped run.
- Record which credential version was active when each tool call was attempted.
Credential revocation is a supporting layer, not a substitute for the tool gate. Revocation systems can have propagation delay, and already accepted requests may still be processing.
Step 5: Stop or isolate compute and the environment
Compute lifecycle is separate from agent-session lifecycle. In OpenAI’s self-hosted environment guidance, the application manages compute and files, and one component should manage each session’s environment while storing the session-to-compute mapping. Deleting a session does not automatically terminate the provider environment.
OpenAI states: “Deleting a session neither stops its environment nor emits a deletion webhook.” Treat this as a provider-specific lifecycle rule, not a universal property of agent platforms. Your infrastructure controller should explicitly stop, isolate, or terminate the worker, sandbox, VM, browser, and network attachments that can continue operating.
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- Coordinate startup and cleanup. If startup is in progress, mark it canceled and wait for the controller to confirm whether compute was created.
- Cancel or isolate network access. Apply egress restrictions or detach the worker from sensitive services before termination when the incident requires immediate containment.
- Terminate through the owning controller. Use the container, VM, sandbox, or host lifecycle API that actually owns the process.
- Verify the result. Query process, environment, and network state rather than assuming that a successful session deletion stopped compute.
- Preserve evidence. Capture logs, tool outcomes, filesystem metadata, and provider request IDs before ephemeral resources disappear, subject to your incident-retention policy.
The lifecycle guide warns that “An idle event alone is not a safe shutdown signal.” Incoming work can race with an idle callback. Coordinate shutdown with queued work and startup, cancel a pending shutdown when a connection is requested or execution begins, and recheck state immediately before stopping compute. If your application cannot make those checks reliable, keeping the environment running is safer than risking a race that leaves work partially initialized.
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OpenAI-specific lifecycle caveats
- For an input-time environment connection, the API can wait up to five minutes before the submission fails. This timeout is specific to the documented OpenAI flow.
- A mid-turn disconnect can cause a tool to fail even if the turn later completes. Inspect individual tool results and the final response.
- After a process crash, pending input recovery is not guaranteed.
- Reusing an environment ID does not restore files in replacement compute.
These details illustrate why a stop handler must inspect actual tool outcomes and infrastructure state rather than rely on a single “run canceled” event.
Step 6: Track in-flight requests and irreversible effects
Maintain an operation ledger for every external action. At minimum, store the run ID, tool, target, request ID, idempotency key, dispatch time, completion state, and whether the provider confirmed acceptance.
When the tool has not started
A deny decision at the tool gate is usually the cleanest case. Record the denial and return control to the stopped run without invoking the external service.
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When the request is in transit
Cancellation may race with network delivery. Mark the operation as unknown until the provider or a reconciliation job confirms whether it was accepted. Do not automatically retry an unknown write unless the API’s idempotency semantics make that safe.
When the action completed
Record the completed side effect and invoke a separately designed compensating action only after an authorized operator confirms that reversal is safe. OpenAI’s Operator system card highlights prompt injection and model mistakes that can be difficult to reverse; the practical response is layered prevention, confirmation for critical actions, monitoring, and a recovery plan—not a promise that an emergency stop can rewind reality.
Step 7: Add human approval for high-impact actions
Use approval gates before actions such as sending external communications, changing production data, transferring funds, deleting records, or actuating equipment. The OpenAI Agents SDK documents resumable human-in-the-loop interruptions through RunState. Its approval surface covers the current agent, handoffs, and nested Agent.as_tool() execution. A needs_approval rule can always require approval or apply conditionally; callable approval rules fail closed when arguments cannot be inspected safely.
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Approval should show the operator the target, arguments, expected effect, identity, and expiry. A rejection should set the run to a denied or stopped state and invalidate any previously prepared tool lease. Approval is not a complete emergency shutdown: it does not necessarily terminate compute, revoke credentials, or stop unrelated processes.
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If an agent controls a robotic arm, factory equipment, or a robot operating in an open environment, software controls should be supplemented by a machine-level emergency stop. NIST lists those systems as examples of physical extensions, but the guidance does not specify a universal switch, wiring arrangement, performance level, or installation.
Choose and install the physical control for the specific machine, energy sources, guarding, restart behavior, and applicable safety engineering requirements. Keep it independent of the model, browser, network, and ordinary application process. Test that pressing it removes hazardous actuation and that restart requires an explicit, authorized procedure. A physical stop does not necessarily remove stored energy or make a machine safe to approach; follow the machine’s documented isolation and lockout procedures.
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Use explicit states
A practical state model can include RUNNING, STOP_REQUESTED, STOPPED, ISOLATED, and RECOVERY_REVIEW. Define which transitions are automatic and which require an authorized person. Do not allow an agent-generated message to transition from a stopped state back to running.
Log the complete event
- Who or what initiated the stop.
- Reason, severity, and incident identifier.
- Run, tenant, worker, session, and environment identifiers.
- Queue messages rejected or quarantined.
- Tool calls denied, in flight, completed, or unknown.
- Credential versions revoked or restricted.
- Worker and compute termination results.
- Physical safety events, where applicable.
- Operator approvals for investigation and restart.
Require deliberate recovery
- Confirm that intake is closed or safely reopened only for a new run generation.
- Reconcile every in-flight operation with the external provider.
- Inspect logs, tool outputs, final model responses, and provider health.
- Remove compromised credentials and verify network and filesystem isolation.
- Document what caused the stop and what control failed to react first.
- Start a fresh, least-privileged run only after an authorized operator approves the recovery.
Testing and failure scenarios
Test the stop path as an operational control, not only as a unit-test branch.
- Stop during model generation: verify that no subsequent tool call is dispatched.
- Stop between authorization and dispatch: force a state change and confirm the final gate denies the call.
- Stop during a long shell command or browser action: verify timeout, process termination, and credential behavior.
- Stop during provider outage: ensure the run remains stopped locally and does not retry blindly.
- Stop during worker startup: confirm that an environment created after the stop is isolated and cleaned up.
- Duplicate stop requests: verify idempotent handling and one consistent terminal record.
- Process crash: confirm that orphaned compute, queued work, and leases are discovered.
- Physical emergency stop: test machine-specific behavior under qualified safety procedures, including controlled restart.
Monitor for denied-call spikes, stale leases, workers that remain alive after a stop, unknown external operations, and re-enqueue attempts. Alerting on those conditions turns a stop from a button into an observable safety system.
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How the controls fit common agent types
Browser agents
Close or isolate the browser session, revoke cookies and tokens, block navigation and form submission at the tool gateway, and preserve the page state and action log. A confirmation prompt before a critical action is useful, but it does not replace the external stop path.
Code and API agents
Cancel the run, deny new executions, revoke API and database credentials, terminate the worker, and reconcile requests whose acceptance is unknown. Restrict network egress and filesystem access during normal operation so the emergency path has fewer capabilities to contain.
Multi-agent systems
Propagate the stop to handoffs, child runs, and nested agent tools. A parent marked stopped is not enough if a child has an independent queue, credential, or worker. Each child should check the same authoritative incident state and use its own generation or lease.
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Combine intake and run cancellation with machine-level actuation control, energy isolation, and qualified inspection. Software should report the physical stop event back to the incident record, but network status must never be the only proof that hazardous motion has ceased.
Standards status and governance
NIST’s Control Overlays for Securing AI Systems project uses SP 800-53 controls and describes implementation-focused overlays for single-agent and multi-agent use cases. The project page was updated January 8, 2026 with a discussion draft for predictive AI. It is an evolving project, not evidence of a finalized universal emergency-stop standard for every AI agent.
Use the overlays and your existing security, safety, and incident-response controls as governance inputs. Define ownership for intake, orchestration, tools, identity, compute, and physical equipment; document service-level expectations for each layer; and rehearse recovery with the teams that operate those systems.
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