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Stop the active run first. Use the product’s Stop or Cancel control, or call the cancellation mechanism in the runtime that launched the agent. If the agent is running in a worker or workflow engine, cancel the job there. Then inspect completed tool calls and external side effects before you restart anything. Cancellation prevents future work; it does not automatically undo records changed, messages sent, or jobs started earlier.
To keep the problem from returning, add a finite turn or iteration limit, trace repeated tool inputs and unchanged results, and require human approval before consequential actions. The exact emergency-stop command is platform-specific, but this sequence works across hosted interfaces, application code, and orchestrated workers.
Contain the loop in the first few minutes
- Cancel the active run or stream. Click the interface’s Stop or Cancel control, invoke the runtime’s cancellation API, or cancel the worker job that owns the execution. Do not assume that closing a browser tab stops a server-side run.
- Prevent new retries. Disable an automatic retry policy, pause the queue, or remove the trigger that is starting another run. If a scheduler or webhook keeps launching the same task, stopping one run will not be enough.
- Record the run identifier and logs. Preserve the trace, tool arguments, tool results, timestamps, and current state. This evidence is needed to distinguish a model decision loop from a faulty tool or orchestrator.
- Check completed effects. Review database writes, file changes, API requests, messages, purchases, cancellations, and child jobs. A cancellation request applies to work that has not run yet; it is not a rollback transaction.
- Do not blindly restart. First identify the repeated action, error, or unchanged observation. Restarting with the same state and prompt can reproduce the loop immediately.
If the process is unresponsive and there is no application-level cancellation, an operator may have to stop the worker or container through its normal operations tooling. Treat that as an emergency containment step: it can leave an external action in an unknown state, so reconcile effects before retrying.
What an action loop looks like
An action loop is an execution pattern in which an agent keeps selecting tools or other actions without reaching a valid finish condition. The actions may be identical, alternate between two calls, or change superficially while the relevant state remains unchanged. A loop can consume model calls and tool charges, delay other work, and repeat side effects.
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Signals in a trace
- The same tool name and materially identical arguments appear on consecutive turns.
- A tool returns an error, but the agent submits the same request without changing the input or fixing the cause.
- The observation sent back to the model is unchanged, so the next decision has no new information.
- The agent reports success, yet the completion condition remains false and the same verification call repeats.
- A retry, queue, or webhook starts a new run as soon as the previous one is cancelled.
- The agent is waiting for approval or a callback, but the orchestration layer keeps polling or resubmitting the action.
These symptoms do not by themselves prove that the model is at fault. A tool may be returning stale data, a state write may be failing, or a workflow may be replaying a step after a timeout. Compare the complete sequence of calls, arguments, results, and state transitions.
Choose the right control
| Control | When it acts | Best use | Limitation |
|---|---|---|---|
| Cancel or stop active run | During an incident | Halt additional calls and actions immediately | The control differs by product and does not undo completed side effects |
| Maximum turns or iterations | At the execution boundary | Guarantee a hard ceiling on runaway work | It can stop a legitimate long task; the limit must fit the task |
| Approval interruption | Before a selected tool action | Prevent unreviewed sensitive side effects | Needs a policy and a rejection path that does not retry the same action |
| Checkpoint and resume | When execution pauses or needs review | Preserve context for deliberate human decisions | State durability and resume semantics depend on the framework and storage |
Use these controls together. A turn cap is a backstop, not evidence that an agent is progressing. Approval is a safety boundary, not a substitute for cancellation. Checkpointing helps you resume intentionally, but it does not make a side effect reversible.
How to cancel an agent on different kinds of systems
Hosted chat or agent interface
Use the visible Stop, Cancel, or Interrupt control while the run is active. Wait for the interface to show that the run has ended, then verify the server-side run status if the product exposes one. If the control is missing or the status remains active, use the provider’s operations or job-management mechanism rather than repeatedly submitting a new message.
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The application that called the agent must retain a handle to the running task, stream, or run identifier and expose a cancellation path. In generic pseudocode, the shape is:
active_run = start_agent(task)
try:
await active_run
finally:
if operator_requested_stop:
await cancel(active_run)The function names above are illustrative, not a universal API. Use the cancellation primitive documented by your SDK or async runtime. Ensure that cancellation is propagated to the model stream, tool call, and any child task; cancelling only the user-facing request can leave a worker running.
OpenAI Agents SDK streams
The OpenAI Agents SDK running-agents documentation describes cancelling a stream during a turn. If you need the same unfinished turn later, the SDK can preserve state so that the run is resumed deliberately. Treat this as cancellation and continuation behavior for that SDK, not as a universal control available in every agent product. After cancellation, inspect what the stream already caused before resuming.
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Workers, queues, and workflow engines
Cancel the job in the system that owns execution. Pause the queue or disable the trigger if new jobs are being created. A web request timeout, a browser close, or a model cancellation may not terminate a detached worker. Record whether the worker acknowledged cancellation and whether any child jobs remain active.
Inspect side effects before doing anything else
Cancellation stops future execution; it does not reverse actions that have already completed. Build a short reconciliation record before a restart:
- List every tool call that reached an external service and mark it as succeeded, failed, timed out, or unknown.
- Check changed rows, files, tickets, messages, calendar events, subscriptions, and launched jobs.
- Look for duplicate requests created by retries or by more than one worker.
- For actions with an idempotency key or transaction identifier, query the target system before submitting another request.
- Escalate unknown outcomes to a person who can confirm the external system’s state.
Do not describe a cancellation as a rollback unless the application actually performed a compensating transaction. If a message was sent, for example, stopping the run cannot unsend it; the recovery may instead require a correction message or a manual record update.
Diagnose the exact repetition
Compare consecutive decisions
Start at the first point where progress stopped. Compare the tool name, arguments, result, relevant state, and the next decision for each turn. A useful trace view places consecutive turns side by side and highlights fields that did not change.
| Trace pattern | What to check next |
|---|---|
| Identical tool and arguments | Why the agent believes the prior result did not satisfy the goal; check whether the result was delivered in the expected schema |
| Same error on every retry | Tool validation, credentials, permissions, required parameters, and retry policy |
| Unchanged observation | Cache invalidation, state writes, polling interval, and whether the tool is reading the intended resource |
| Alternating calls | Two tools may be undoing each other, or each may be waiting for a state change the other never makes |
| Completion claim followed by verification | Definition of done, eventual consistency, and whether verification can observe the write |
| New runs after cancellation | Scheduler, webhook, queue retry, or supervising process that is relaunching the task |
Separate model behavior from tool behavior
Run the tool independently with the exact arguments captured in the trace. If it fails or returns stale data outside the agent, fix the tool or its integration first. If it behaves correctly, inspect the prompt, state representation, and completion test that guide the agent’s next action. This split prevents you from adding more model instructions to a deterministic integration failure.
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Preserve the failing state
Save the prompt or task input, tool schemas, relevant conversation or graph state, environment version, and trace identifier. Redacting secrets is important, but do not remove the fields needed to reproduce the decision. A restart from a clean state can hide the condition that caused the loop.
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Add a hard execution bound
Configure a finite maximum number of turns, graph iterations, or tool calls for every production run. Decide in advance what happens at the boundary: stop, persist diagnostics, mark the run as needing review, and alert an operator. Do not silently start another run with the same state.
OpenAI Agents SDK limit
In the OpenAI Agents SDK, exceeding the max_turns limit raises MaxTurnsExceeded. The documentation also states that max_turns=None disables the limit. Leaving the limit disabled should be an explicit decision for a controlled use case, not an accidental default in a production workflow.
run = start_agent(task, max_turns=chosen_limit)
try:
result = await run
except MaxTurnsExceeded:
save_diagnostics(run)
route_to_human_review(run)The names in this example show the handling pattern; use the SDK’s actual run and exception interfaces in your implementation. Choose a task-appropriate bound from observed work, then leave headroom for legitimate steps. No universal safe iteration count has been established by the official material, so do not copy a number from an unrelated task.
Make the boundary useful
- Include the current turn, last tool, and last result in the failure record.
- Expose the stop reason to operators instead of reporting a generic failure.
- Keep the run state available for diagnosis, but require an explicit decision before resuming.
- Alert on repeated boundary hits so the system does not quietly accumulate failed runs.
Pause before risky actions with human approval
Place an approval gate before a side effect such as editing records, running shell commands, cancelling an account, sending an external message, or calling a sensitive service. A final review after the tool has acted cannot prevent the original effect.
OpenAI’s guidance distinguishes automatic guardrails from human review: guardrails perform checks, while approval determines whether a run should continue, pause, or stop. In the OpenAI Agents SDK, pending approvals are represented as interruptions; after a person approves or rejects, the run can resume from saved state. Configure rejection so it records the decision or takes an alternate path rather than submitting the identical call again.
LangGraph documents the same checkpoint-and-review pattern with interrupt(). The graph pauses and checkpoints state while waiting for human input, then resumes with that input. The pause may last minutes, hours, or days, so the storage and worker lifecycle must support that duration.
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What an approval request should show
- The exact tool and arguments that will be executed.
- The affected account, records, files, or recipients.
- The reason the agent selected the action and the evidence it used.
- Whether the action is idempotent and what happens if it is approved twice.
- Clear Approve, Reject, and Escalate outcomes.
Make retries safe instead of infinite
Retries should respond to a classified, temporary failure, not to every exception or an unchanged result. At the tool boundary, set a retry count and a backoff policy, then return a terminal error that the agent can understand. If the same input fails again, route it to review instead of asking the agent to repeat it without new information.
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Give an external operation a stable request or idempotency key derived from the business action, not from the individual retry. On a timeout, query the target system with that key before issuing another request. This avoids creating duplicates when the first request succeeded but its response was lost.
Define a real completion condition
Write the condition in terms of observable state: a record has a required status, a file exists with the expected checksum, or an approval has been recorded. Avoid a vague instruction such as keep trying until it works. If the condition cannot become true because of permissions, invalid data, or an unavailable dependency, end the run with a diagnostic state.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use checkpointing and deliberate resume
Checkpointing preserves the agent’s state at a pause or interruption so a person can decide what to do next. It is useful after cancellation, an approval request, a worker restart, or a temporary dependency outage. It is not permission to resume automatically from the same failing step.
Resume choices
- Continue: use only when the side effect was not executed and the inputs are still valid.
- Skip: use when the action was completed outside the run or is no longer needed.
- Modify: change the arguments or state after fixing the cause.
- Abort: close the run and record why it cannot safely continue.
When a framework replays work from a checkpoint, make sure replayed steps are idempotent or explicitly marked as already completed. Otherwise a resume can duplicate the very action that caused the incident.
Control cost and capacity while you investigate
Contain spend without destroying the evidence needed to diagnose the loop. Cancel the active run, pause its trigger, and apply a temporary concurrency limit to the affected workflow. Keep enough logging to see tool names, argument hashes, result status, turn number, and latency; redact secrets and unnecessary payloads.
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Useful alerts include a run approaching its turn limit, repeated identical tool arguments, repeated identical error codes, an unusual rate of cancelled runs, and a queue that keeps redelivering the same task. Alert thresholds should be based on your task’s normal behavior rather than a universal number that has not been established.
Design a production-safe agent runner
A robust runner separates decision-making from execution and makes every boundary observable.
- Admission: assign a run identifier, set a turn or iteration ceiling, and attach a cancellation handle.
- Planning: let the agent propose the next action while recording the state it used.
- Policy check: apply automatic guardrails and pause for human approval when the action is sensitive.
- Execution: call the tool with an idempotency key, timeout, and narrowly scoped permissions.
- Observation: validate the result against a schema and update durable state.
- Progress test: compare the new state with the prior state; stop or escalate if the agent is repeating without progress.
- Completion: mark the run complete only when the observable success condition is true.
For long-lived workflows, the OpenAI Agents SDK documentation identifies integrations with Dapr, Temporal, Restate, and DBOS for durable execution and human-in-the-loop patterns. Their suitability, configuration, and commercial terms depend on your environment; select an orchestration system that can persist state, deliver cancellation, and expose failed or waiting runs to operators.
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- Identify the run, worker, and trigger responsible for the repeated actions.
- Cancel the active run at the layer that owns execution.
- Pause automatic retries and upstream triggers.
- Capture the trace and preserve the current state.
- Reconcile every external side effect, including unknown outcomes.
- Compare repeated calls, arguments, results, and state transitions.
- Fix the failing tool, schema, state write, completion test, or retry policy.
- Set or verify a finite turn or iteration limit.
- Add approval before the risky action if a person should decide.
- Resume from a checkpoint only after choosing Continue, Skip, Modify, or Abort.
- Watch the resumed run and confirm that its state changes toward completion.
- Document the cause and add an alert or regression test for the same pattern.
When to keep the run stopped
Leave the run stopped when the external outcome is unknown, the same side effect is not idempotent, credentials or permissions are failing, the completion condition is ambiguous, or the agent has no new information with which to make a different decision. A human decision and a corrected state are safer than a faster retry.
The durable solution is not a single kill switch. It is an immediate cancellation path backed by side-effect reconciliation, a finite execution bound, trace-based progress checks, and approval gates before consequential tools. Together these controls stop a runaway agent now and make the next run explainable and recoverable.
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