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Stop an AI agent loop from outside the agent, then recover in two tracks: restore code and agent state from a known-good checkpoint, and separately reconcile every external action the run completed. A message telling the same agent to stop is not a dependable kill switch when it is still invoking tools. Freeze writes, cancel or terminate the run through the orchestrator, revoke risky credentials, preserve the trace, and only restart after a read-only verification.
What an AI agent runaway loop is
An agent run is a loop when a runtime repeatedly alternates between model decisions and tool calls without reaching a valid terminal condition. The model may keep retrying a failing API, re-open the same files, hand work to another agent, or generate new tasks faster than it completes them. A loop can be busy and expensive even when every individual step appears reasonable.
The dangerous distinction is between semantic completion and runtime completion. The model might say “done,” while a tool call is still in flight, a queued retry is pending, or a child run retains permission to write. Conversely, a model can keep claiming progress while producing no meaningful change. Treat completion as true only when the runtime’s completion predicate, tool state, and write state agree.
- Typical symptoms: rapidly increasing tool-call counts, repeated identical arguments, recurring timeouts, growing child-run trees, unchanged files despite many steps, token or spend alerts, and workers that ignore conversational stop requests.
- Primary risk: each iteration can mutate files, databases, cloud resources, messages, or purchases before anyone notices.
- Operational principle: the authority that can stop a run must be outside the model context and outside the tools the model can invoke.
Microsoft Agent Framework documentation states: “Always bound autonomous loops. A completion condition can fail, a model can stall, and an evaluator can be probabilistic.” That is a design requirement, not merely a tuning suggestion.
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Immediate incident procedure
Use this order when a run is looping. Keep the incident record open until both the agent state and external side effects have been checked.
1. Declare the incident and freeze writes
Announce that the run is under containment and disable tools that can mutate anything. Pause file writes, database updates, cloud-resource operations, outbound messages, purchases, deployments, and recursive handoffs. Leave read-only tracing and status queries available so you can observe the run without expanding its blast radius.
If your runtime has tool groups, remove the write-capable group rather than shutting down all observability. If permissions are attached to a worker identity, replace that identity’s policy with a read-only policy. Do not rely on a prompt such as “do not make changes”; the control must be enforced by the orchestrator or authorization layer.
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2. Stop the run from outside its context
Use the orchestrator’s cancel or abort operation first. If cancellation cannot regain control, pause the queue, terminate the worker, or shut down the sandbox that owns the process. Alineo’s engineering guidance captures the reason: “So the kill switch has to live in something the agent can’t reach.”
Do not send a stop message through the same conversation and call that containment. A looping agent may never process the message, may interpret it as another task, or may call a tool before it reads the message. The control-plane action should not depend on model cooperation.
3. Bound the blast radius
- Revoke temporary credentials and session tokens used by the run, especially credentials for production systems.
- Cap remaining token and monetary budgets, and disable automatic retries.
- Block recursive handoffs and prevent queued child runs from starting.
- Apply network egress restrictions if the worker can reach unapproved services.
- Record the time containment began so actions after that point can be distinguished from actions before it.
Credential revocation should be scoped to the affected run where possible. Rotating every credential can create a second outage, while leaving a leaked or still-active token in place allows the loop to continue through another worker.
4. Capture evidence before cleanup
Save the run ID, parent and child IDs, model and tool versions, system and user prompts, tool arguments and results, timestamps, approval decisions, files changed, and external request or transaction IDs. Preserve queue messages, retry counters, cancellation responses, and worker logs as well.
Capture the last known-good checkpoint identifier before restoring anything. A trace copied after rollback may no longer show the original file hashes or pending operations. Store the evidence immutably or in a location the affected credentials cannot overwrite.
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5. Choose a recovery point
Select a specific Git commit, agent checkpoint, filesystem snapshot, database transaction boundary, or provider version that you can identify by ID and timestamp. “Yesterday’s backup” is not precise enough if several runs changed the system during that period.
Restore the smallest affected scope first. If only a workspace was changed, restore that workspace instead of rolling back an entire environment. If a database transaction was committed in several steps, use the database’s transaction or point-in-time recovery mechanism and inspect dependent records before reopening writes.
6. Verify before restart
Run a read-only replay or dry run against the restored state. Confirm that the completion predicate can become false, that no stale worker still owns a write claim, and that revoked credentials are rejected. Restart with lower budgets and explicit human approval for high-impact tools. Keep the first restart isolated so you can stop it without affecting unrelated sessions.
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Why cancellation has to be a control-plane operation
An agent runtime usually contains several independent actors: a model loop, a tool executor, a queue, workers, and sometimes child agents. Cancelling only one actor leaves the others able to finish or retry work. Define ownership clearly:
| Layer | What it controls | Containment action |
|---|---|---|
| Run controller | Model turns, completion checks, and cancellation state | Mark the run cancelled and reject new turns |
| Queue | Pending retries, scheduled jobs, and child runs | Pause the queue and remove or quarantine pending work |
| Worker or sandbox | In-flight code and tool processes | Terminate the worker or sandbox when cooperative cancellation fails |
| Authorization layer | Credentials and permission to mutate systems | Revoke or narrow tokens and policies |
| Write store | Transcripts, files, records, and checkpoints | Reject stale writers and restore a selected version |
OpenAI’s runner raises MaxTurnsExceededError when its configured turn boundary is reached. That exception is useful only if the controller treats it as a terminal failure, records the partial state, and prevents automatic requeueing. A cancellation response should also record whether the model was idle, waiting for a tool, or interrupted during a tool call.
Cancellation during a tool call is inherently different from cancellation between turns. If the tool is local and supports process termination, the worker may stop it. If the tool has already sent an external request, cancellation can stop waiting for the response but cannot guarantee that the remote service did not execute the request. Always reconcile the remote request ID.
Roll back code and agent state without pretending side effects disappeared
Code and files
Use version control or a filesystem snapshot to restore the known-good revision. Before changing it, export a diff of every modified file, including generated files and configuration. Check for untracked files, changed permissions, deleted files, and secrets written outside the repository.
- Quarantine the affected workspace and keep the original copy read-only.
- Record the commit, snapshot, or file-hash set you intend to restore.
- Restore that exact revision in a clean workspace rather than layering a reverse patch over unknown changes.
- Run tests, linters, configuration validation, and secret scanning without granting write permissions.
- Compare the restored file hashes with the checkpoint and approve the workspace for a controlled restart.
Agent transcripts, memory, and checkpoints
Restore the conversation or state snapshot that preceded the looping decision. Remove queued child tasks derived from the bad state, and invalidate caches that contain tool results from the contaminated run. If your runtime stores summaries or long-term memory separately from the transcript, roll those records back or mark them untrusted; otherwise the next run may repeat the same faulty plan.
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Databases and durable application state
Prefer a transaction rollback when all mutations are inside one transaction. For committed changes, use point-in-time recovery, a versioned record restore, or an application-specific compensating migration. Inspect foreign keys, search indexes, event streams, and caches that may have consumed the changed record. Restoring one table without its downstream projections can produce a state that looks valid but is internally inconsistent.
External actions require reconciliation
Restoring code or agent state cannot automatically undo an email, purchase, deployment, account change, or API request that already completed. Build a separate ledger from tool results and provider request IDs, then classify each action:
| Action status | What to do |
|---|---|
| Not sent | Confirm from the trace and provider logs; no compensating action is needed. |
| Sent, outcome unknown | Query the provider by idempotency key or request ID before retrying. |
| Completed and reversible | Use the provider’s documented cancellation or refund operation, with human approval. |
| Completed and irreversible | Escalate for manual remediation, disclosure, or business recovery. |
Idempotency keys prevent a recovery replay from creating a second charge or duplicate job. Where a provider has no idempotency support, require manual review before any retry.
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Framework capabilities to compare before choosing a runtime
Evaluate runtimes on control and recovery behavior rather than on model quality alone.
| Runtime or framework | Relevant documented behavior | Questions to verify in your deployment |
|---|---|---|
| OpenAI Agents SDK runner | Runs a model/tool/handoff loop and enforces a maximum-turn boundary, raising MaxTurnsExceededError at the limit. |
Who receives the exception, are retries disabled, and how are in-flight tools reconciled? |
| Microsoft Agent Framework | Exposes explicit loop evaluators and max_iterations; continuation depends on a completion predicate. |
Does the predicate inspect durable tool state, and is the iteration count shared across child runs? |
| Cloudflare Agents | Provides maxRecoveryWork as a runaway-loop guard, plus a no-progress timeout and bounded out-of-memory retries. |
What counts as progress, and are recovery attempts included in your cost and time budget? |
| GitHub Copilot SDK | Identifies the CLI as the orchestrator of the tool-use loop. The session.idle event is the reliable processing-end signal; task-complete is a separate semantic signal. |
Do you wait for idle before releasing locks, and how do you handle a task-complete claim while tools remain active? |
| OpenClaw | Documents timeout aborts, serialized per-session queues, and an activeWriterRunId claim so a superseded run cannot commit stale transcript data. |
Are writer claims enforced for every durable store, and what happens to a claim after a worker crash? |
Use the same test plan for each implementation: force a tool timeout, kill the worker during a write, exceed the turn limit, create a child-run storm, and send a cancellation while a remote request is pending. Record the resulting state, audit trail, and duplicate-action risk.
Guardrails that prevent recurrence
Finite turns and iterations
Set a hard maximum for model turns or loop iterations. Microsoft’s max_iterations and OpenAI’s maximum-turn boundary are examples of this control. Count turns across handoffs and retries, not only within one function. When the limit is reached, fail closed: stop issuing tools, persist the partial result, and require a human or a new run to continue.
No-progress and work budgets
A turn limit does not detect a fast loop that consumes resources before reaching the limit. Add a work budget for tool calls, wall-clock time, tokens, money, and recovery attempts. Cloudflare documents maxRecoveryWork, a no-progress timeout, and bounded out-of-memory retries as separate protections. Define progress using observable state changes, not a model-generated explanation.
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Make the runtime continue only while a predicate returns true. A robust predicate can require all of the following:
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- the requested artifact exists and passes validation;
- no tool call is pending or scheduled;
- the write transaction committed successfully;
- required approvals are present; and
- the run has not exceeded its time, turn, or spend budget.
“The model says it is done” should be advisory. The predicate should be evaluated by trusted runtime code with access to durable state.
Approval gates and least privilege
Put destructive tools behind an approval gate and issue short-lived, narrowly scoped credentials. Separate read, draft, and commit capabilities. A model that can inspect an account should not automatically be able to purchase, deploy, delete, or message from that account. Require a fresh approval when the target, amount, environment, or recipient changes.
Queue serialization and stale-write protection
Serialize runs that share a session or workspace. Track the active writer with a run identifier and reject commits from superseded or cancelled runs. OpenClaw documents this pattern with an activeWriterRunId claim. Apply the same check to transcripts, files, databases, and event publishers; protecting only the chat log leaves other stores vulnerable to stale writes.
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Provide an operator-facing cancel endpoint, a worker watchdog, and a sandbox shutdown path. The watchdog should trigger when heartbeats stop, progress remains unchanged for a defined interval, or resource use crosses a threshold. Cancellation should be idempotent: repeating it must not create a new run or alter application data.
Audit-quality traces
Log every model turn, tool name, arguments, result, approval, retry, handoff, credential identity, and state transition with a timestamp and run ID. Redact secrets while retaining enough structure to identify duplicate requests. A trace that records only final text cannot explain whether a purchase was attempted, completed, or repeated.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Testing a stop-and-rollback runbook
Exercise the controls regularly in a non-production environment with fake credentials and reversible test resources.
- Loop test: configure a tool to return the same failure repeatedly and verify that the no-progress guard stops the run.
- Turn-limit test: exceed the configured maximum and confirm a terminal error is recorded and no retry starts.
- Cancellation test: cancel during model reasoning, between turns, and during a tool call; record which process and queue entries remain.
- Worker-loss test: terminate the worker during a write and verify that a replacement cannot commit with a stale writer claim.
- External-side-effect test: send a test request, lose the response, and confirm recovery queries the idempotency key before retrying.
- Rollback test: restore code, memory, and database state from a named checkpoint, then run read-only validation.
- Evidence test: ensure operators can retrieve run IDs, parent-child relationships, arguments, results, approvals, and provider request IDs after cleanup.
Document who may invoke each stop action, how long credential revocation takes, where traces are retained, and which business owner approves compensating actions. A runbook that depends on one engineer’s undocumented knowledge is not an operational control.
Troubleshooting common failure modes
The cancel button reports success but tools continue
Check whether the button cancelled only the model loop while a worker or queue continued. Inspect active processes, pending jobs, and child-run IDs. Pause the queue, revoke credentials, and terminate the worker if necessary. Treat the run as active until all write-capable actors are stopped.
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The agent restarts immediately after reaching its limit
Look for a retry policy that classifies the limit error as transient, or a supervisor that requeues every failed job. Mark turn-limit and cancellation outcomes as non-retryable unless a human explicitly starts a new run with a new budget.
Rollback restored files but the application is still wrong
Inspect databases, caches, indexes, event streams, generated artifacts, and external services. The workspace may be clean while a deployment, message, or remote record remains changed. Reconcile each external request separately using its idempotency key or request ID.
The next run repeats the same bad plan
Invalidate contaminated summaries, memory entries, cached tool results, and queued child tasks. Start from a clean transcript and verify that the restored checkpoint is actually the one used by the worker. Add a completion predicate or approval gate for the decision that caused the loop.
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Correlate the agent trace with provider logs and request IDs. If the provider offers neither idempotency keys nor status lookup, stop automatic retries and require manual confirmation. Unknown outcome is not permission to send the request again.
Operational checklist
- External cancel, queue pause, worker termination, and sandbox shutdown paths are tested.
- Every autonomous loop has finite turns or iterations and a no-progress or work budget.
- Completion predicates inspect durable state instead of trusting model text.
- Write-capable tools require scoped credentials and approval where impact is high.
- Runs sharing a workspace are serialized and protected against stale writers.
- Traces retain run IDs, child IDs, tool arguments and results, approvals, timestamps, and external request IDs.
- Known-good checkpoints are named, restorable, and periodically tested.
- Recovery procedures distinguish reversible code changes from irreversible external effects.
- Retries after cancellation, limit errors, and unknown external outcomes are disabled by default.
Frequently Asked Questions
Can I stop a runaway agent by sending it a message in the same chat?
No. The run may never process that message or may invoke another tool first. Use an orchestrator, worker, queue, or sandbox control outside the agent context.
Does cancelling a tool call undo what the tool already sent?
Not necessarily. Cancellation can stop your worker from waiting, but a remote request may already have been accepted. Check the provider with its request ID or idempotency key before retrying.
What should be restored first: files or the database?
Restore the smallest affected scope from a precisely identified checkpoint, then validate dependencies. If mutations crossed files and durable records, coordinate both restores rather than assuming one rollback fixes the other.
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Require the runtime’s completion predicate to be false, no tools or child runs to be pending, and all writes to be committed. A semantic task-complete message alone is insufficient.
What is the safest default after a loop is contained?
Keep write tools disabled, preserve the trace, perform a read-only replay from a known-good checkpoint, and restart with lower budgets and human approval for high-impact actions.
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