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Stop the active run first. Use the product’s Stop or Cancel control, or call the cancellation method in the runtime that launched the agent. Then inspect the tool calls and side effects that already completed. Do not restart automatically: identify the repeated action, unchanged observation, or recurring error before allowing the workflow to run again.
The exact emergency-stop command is platform-specific. A finite turn or iteration limit, approval before risky tools, and checkpointed state for deliberate resumption prevent the same failure from becoming an expensive or destructive loop.
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What an action loop is—and why stopping it is urgent
An action loop occurs when an agent repeatedly selects the same tool or sequence without making meaningful progress. Typical patterns include issuing an identical API request, retrying a failed shell command, reopening the same record, or observing an unchanged result and choosing the same next step.
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There is no universal emergency command. An agent in a web console, a background worker, a graph runtime, and a custom application will expose different controls. Stop it at the layer that owns execution: the interface, job runner, queue, workflow engine, or SDK run object.
Emergency procedure: contain the run before debugging
- Cancel the active run or stream. Click the application’s Stop or Cancel control, or invoke the cancellation operation provided by the runtime that started the run. If a worker owns the process, cancel the job there rather than merely closing a browser tab.
- Prevent new work from being dispatched. If cancellation is asynchronous, pause the queue, disable the worker, revoke the job, or remove the run’s ability to call tools until its status is confirmed as cancelled. Follow the current documentation for your platform; control names and timing differ.
- Record the run state. Save the run identifier, timestamp, model response, tool name, arguments, results, error messages, and iteration or turn number. These details show whether the agent was repeating an input, retrying an error, or receiving an unchanged observation.
- Audit completed side effects. Check modified records, sent messages, created files, launched jobs, payments, permissions, and other systems the tools could reach. A cancellation request cannot undo an action that the external system already accepted.
- Disable automatic retries temporarily. Retry middleware, queue redelivery, and supervisor restarts can immediately recreate the loop. Stop those mechanisms while you investigate.
- Resume only with an explicit decision. Preserve the state if the task should continue, but do not submit it again until you have changed the failing condition, added a bound, or approved the next action.
How cancellation and resumption work in common runtimes
OpenAI Agents SDK streams
The OpenAI Agents SDK running-agents documentation describes cancelling a stream in the middle of a turn. If continuation is appropriate, saved state can be used to resume the unfinished turn later. Treat this as a cancellation-and-continuation capability of that SDK, not as a universal behavior for every agent product.
When you resume, first inspect the last tool call and its result. If the tool succeeded but the response was lost, resuming blindly may duplicate the side effect. Prefer an idempotency key, a status lookup, or a human check that confirms whether the external action happened.
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LangGraph documents an interrupt() mechanism that pauses execution and checkpoints state while waiting for human input. The graph can resume with that input, including a decision to reject the proposed action. This is useful when the workflow needs to wait for a person for minutes, hours, or days rather than spin in a worker.
Custom workers and job queues
For a home-grown runner, cancellation normally requires a cooperative signal. The worker must check a cancellation flag between model calls and before each tool call, then close the run cleanly. A process kill is a last resort: it may stop the worker while an external request is in flight and leaves reconciliation work behind.
while run.status == "active" and turns < MAX_TURNS:
if cancellation_requested(run.id):
mark_cancelled(run.id)
break
decision = model.next_action(state)
if decision.requires_approval:
pause_for_approval(run.id, decision)
break
result = execute_tool(decision)
state = update_state(state, result)
turns += 1The example illustrates control points, not a drop-in command. Your queue, SDK, and persistence layer determine the actual APIs.
Controls that keep a loop from running forever
| Control | When it acts | Best use | Limitation |
|---|---|---|---|
| Cancel or stop active run | During an incident | Halt further calls immediately | Product-specific; completed side effects remain |
| Maximum turns or iterations | At the execution boundary | Guarantee a hard ceiling on runaway work | Can stop a legitimate long task; the limit must fit the task |
| Approval interruption | Before a selected tool action | Prevent unreviewed sensitive changes | Requires an approval policy and a non-looping rejection path |
| Checkpoint and resume | When work pauses or needs review | Preserve context for deliberate continuation | Behavior depends on the framework and state storage |
Set a finite turn or iteration limit
A turn cap is the simplest hard backstop. Decide what counts as a turn in your system—usually one model decision plus any resulting tool calls—and set a finite maximum for every production run.
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In the OpenAI Agents SDK, exceeding max_turns raises MaxTurnsExceeded. The documentation states that max_turns=None disables the limit; leaving it unset or unlimited removes the protection. Catch the exception at the job boundary, record the run as stopped for exceeding its budget, and alert a person or scheduler instead of silently retrying.
Choose a task-appropriate bound
- Estimate the number of distinct steps a successful task requires.
- Add a modest margin for tool errors and clarification, not an unlimited retry allowance.
- Use a lower cap for side-effecting tasks than for read-only analysis.
- Make the cap configurable per workflow and environment.
- Expose the current turn count in logs and monitoring.
No single iteration count is safe for every task. A research workflow, a database migration, and a one-step notification have different legitimate lengths. A cap should stop a runaway process, not serve as evidence that the agent is making progress.
Define the limit-exceeded path
When the limit is reached, persist the transcript and state, mark the run as requiring review, stop dispatching tools, and include the last action in the alert. Do not send the same prompt back into the same runner automatically. A human can either correct the input, resume from a checkpoint, or terminate the task.
Require approval before risky tools
Automatic guardrails can validate inputs, but a human approval gate is appropriate for actions with meaningful side effects: editing or deleting records, running shell commands, cancelling jobs, sending messages, changing permissions, or calling a sensitive external service.
Place the approval immediately before the side effect. A final review after the tool has run is only an audit, not a prevention mechanism.
Design the approval request
- Show the exact tool name and arguments, not only the agent’s natural-language summary.
- Explain the expected external effect and identify the target account, record, file, or environment.
- Display relevant preceding observations so the reviewer can detect stale state.
- Offer explicit Approve and Reject outcomes, with a reason field for rejection.
- Expire requests after a defined period rather than leaving workers blocked indefinitely.
The OpenAI Agents SDK exposes pending approvals as interruptions and supports resuming from saved state after a decision. LangGraph’s checkpointed interrupt() pattern provides a similar pause-and-resume shape. In either framework, a rejection must update the state or terminate the run; it must not feed the identical rejected action back to the model as an automatic retry.
Diagnose the exact loop pattern
After containment, compare consecutive steps in the trace. A useful trace includes timestamps, turn numbers, tool names, normalized arguments, returned values, error codes, and the state fields used to choose the next action.
Identical tool calls
If the tool name and arguments are identical, the agent may not be receiving the result, may be losing state between turns, or may be instructed to retry without a limit. Add a duplicate-call detector at the runtime boundary. For side-effecting tools, use idempotency keys and query the external system before submitting the same operation again.
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Look for the same status code, validation message, permission failure, or timeout. Retrying a deterministic error rarely helps. Classify errors into retryable and non-retryable categories, cap exponential backoff, and route authentication, schema, and permission failures to a human or corrective workflow.
Unchanged observations
An agent can call a read tool repeatedly because the world has not changed or because it cannot interpret the response. Compare the observation returned on each turn. If it is unchanged, require a different action, ask for clarification, or terminate with a clear reason rather than polling indefinitely.
Successful tool call, failed interpretation
Sometimes the external action succeeds but the agent receives an empty, malformed, or ambiguous result and repeats the request. Make tools return structured status, identifiers, and an explicit already-completed result. Store the external operation ID so the next turn can look up status instead of creating another operation.
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State or prompt regression
Check whether a summarizer dropped a required field, a new message overwrote progress, or a restart reloaded an old checkpoint. Log the state hash or selected progress fields at every turn. If the state moves backward, fix persistence or merge logic before changing the prompt.
Build loop detection at the tool and runtime boundaries
Model instructions alone are not a safety mechanism. Add deterministic checks outside the model.
Detect repeated calls
Normalize arguments by sorting object keys, removing irrelevant formatting, and redacting secrets before hashing. Track recent hashes per run. A configurable threshold can pause the run when the same call occurs repeatedly, especially when the result is unchanged.
Detect no-progress windows
Define progress for the workflow: a changed record version, a completed checklist item, a new external status, or a reduced set of pending tasks. If several turns produce no progress, stop or request approval. Do not rely only on elapsed time; a fast loop can spend a large budget before a time limit fires.
Separate read and write budgets
Give read-only polling a bounded budget and impose a stricter budget on writes. Require an idempotency key for create, send, charge, delete, and other non-repeatable operations. Reject a write when the key has already been used unless the caller is explicitly checking status.
Make cancellation observable
Record when cancellation was requested, when the worker acknowledged it, and whether a tool call was in flight. Expose these states to operators so a “stopped” button is not mistaken for proof that every downstream request has finished.
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Resume safely after a cancellation
- Reconcile external state. Query each system affected by the last in-flight or completed tool call.
- Mark completed work. Update the checkpoint with confirmed outcomes and operation IDs.
- Change the failure condition. Correct credentials, input data, schema handling, prompt state, retry policy, or tool implementation as appropriate.
- Choose a continuation point. Resume after the confirmed successful step, not before it. If the state is ambiguous, start a compensating or reconciliation workflow instead.
- Require approval for the first side effect. Let an operator verify that the proposed next action is still valid.
- Keep the old trace attached. A resumed run should reference the cancelled run so duplicate activity and root cause remain auditable.
Production architecture for long-running agents
Long-lived workflows need durable state, cancellation, retries, and human review as first-class features. The OpenAI Agents SDK documentation names integrations with Dapr, Temporal, Restate, and DBOS for durable or long-running workflows and human-in-the-loop patterns. These are orchestration options to evaluate; their plans, pricing, and suitability depend on your deployment and are not established here.
Regardless of the orchestration layer, use the same separation of responsibilities:
- Planner: proposes the next action and explains its expected outcome.
- Policy layer: enforces turn limits, permissions, approval requirements, and tool allowlists.
- Executor: performs one bounded tool call and returns structured results.
- State store: checkpoints progress, operation IDs, approvals, and cancellation status.
- Observer: emits traces, detects repetition, and alerts on limit breaches or stalled progress.
- Reconciler: verifies external effects after timeouts, cancellation, or worker failure.
Common mistakes and their fixes
Closing the interface and assuming the run stopped
A browser tab may only disconnect your view. Confirm the run status in the owning runtime and verify that workers stopped accepting new tool calls.
Raising the turn limit to make the task “finish”
A higher limit can hide a state or tool defect and increase cost. Inspect the trace first; raise the bound only when the task genuinely needs more steps.
Retrying every error
Retry only transient failures, with a bounded count and backoff. Route deterministic errors to correction or review.
Approving a vague request
Approval without exact arguments shifts risk to the reviewer. Show the concrete operation and its target.
Adding a human review after the action
Post-action review cannot prevent the side effect. Move the interruption before the tool call.
Assuming cancellation reverses changes
Cancellation controls future execution. Reconcile and, where supported, run an explicit compensating action for effects that already occurred.
Operational checklist
- Can an operator cancel a run without killing unrelated workers?
- Does the worker acknowledge cancellation between model and tool calls?
- Is a finite turn or iteration limit configured?
- Does exceeding the limit create an alert and preserve diagnostics?
- Are sensitive tools paused for approval before execution?
- Does rejection terminate or alter the plan instead of retrying identically?
- Are tool calls idempotent or protected by operation keys?
- Can operators see completed side effects and in-flight requests?
- Is state checkpointed before and after side-effecting tools?
- Can a cancelled run resume from a verified checkpoint?
- Are repeated calls, repeated errors, and unchanged observations detected?
- Are automatic retries and queue redeliveries bounded?
Key takeaway
Stopping a stuck agent is a two-stage job: cancel the active execution, then reconcile what it already did. Prevent recurrence with finite turns, pre-action approval, durable checkpoints, idempotent tools, and trace-based detection of repetition or no progress. These controls work together: a cap limits damage, approval prevents sensitive mistakes, and state plus reconciliation makes any later continuation deliberate rather than another blind retry.
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