The Tool Desk
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A message telling the agent to stop is not a dependable kill switch when the same loop is misbehaving. Safe operations require bounded turns or iterations, a no-progress or work budget, an explicit completion predicate, stale-write protection, and human approval for high-impact tools.
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What an AI agent runaway loop is
An agent loop normally alternates between model reasoning and tool execution: the model proposes an action, a runner invokes a tool, the result returns to the model, and the cycle continues until a completion condition is met. A runaway loop is that same cycle continuing without reaching a valid terminal state. It may repeat identical calls, bounce between subtasks, retry a failing operation forever, or keep handing work to child agents.
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The loop can consume tokens and compute, modify files or databases, create duplicate records, send repeated messages, or spend money. The visible symptom is often not a crashed process but a run that remains active while making no meaningful progress.
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Signs that a run is no longer making progress
- The same tool name and nearly identical arguments appear repeatedly in the trace.
- The agent alternates between two or more tools without changing the state needed for completion.
- Retries continue after the error is deterministic, such as a missing permission or invalid parameter.
- A child run is started for work that the parent already assigned, producing recursive handoffs.
- Files, records, or messages are being changed, but the completion predicate never becomes true.
- Token, time, memory, or spend counters rise while the output remains unchanged.
Treat any one of these patterns as an incident when the run has write access. Waiting for the model to decide that it is stuck increases the possible blast radius.
Immediate incident procedure
Use the following order. It prioritizes preventing additional side effects, preserving evidence, and only then restoring state.
- Declare the incident and freeze writes. Disable or pause tools that can mutate files, databases, cloud resources, queues, messages, or purchases. Leave read-only tracing and status endpoints available. If tools are grouped by capability, disable the write group rather than taking down observability.
- Stop the run from outside its context. Call the orchestrator’s cancel or abort operation. If the worker does not acknowledge cancellation, terminate the worker process, pause the queue, or shut down the sandbox that contains it. Alineo’s engineering guidance explains why: a looping agent may never process a stop message delivered through its own context, so the kill switch must live in something the agent cannot reach.
- Bound the blast radius. Revoke short-lived credentials, lower or disable spend limits, block retries, and prevent recursive handoffs. Disconnect write-capable network routes if your platform permits it. Revocation stops future authorized calls; it may not undo a request already accepted by an external service.
- 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, approvals, changed files, database transaction IDs, and external request or idempotency IDs. Export logs to storage the agent cannot modify.
- Choose a recovery point. Identify the last known-good version-control commit, filesystem snapshot, agent checkpoint, or database transaction boundary. Record exactly which components will be restored and which will be reconciled manually.
- Verify before restarting. Perform a small read-only replay or dry run. Check that the completion predicate can become false and then true, that limits are active, and that high-impact tools require approval. Restart with lower budgets and a narrow scope.
Why an in-context stop command is unreliable
A stop request sent as another message to the same agent is just another event in the loop. The model may ignore it, fail to receive it while a tool call is running, or produce another tool call before it evaluates the request. A tool can also be blocked, delayed, or compromised. Cancellation therefore needs an authority outside the model, prompt, and tool set.
Use a layered kill switch
| Layer | What it does | When to use it |
|---|---|---|
| Run cancellation | Marks the run aborted and asks the runner to stop scheduling new model or tool steps. | First action when the orchestrator is responsive. |
| Worker termination | Stops the process or container executing the loop. | Use when cancellation is not acknowledged or a call is wedged. |
| Queue or session pause | Prevents queued retries, child runs, and new work for the affected session. | Use when multiple jobs can continue the same workflow. |
| Tool and credential quarantine | Removes the ability to write or authenticate to sensitive systems. | Use in parallel with cancellation when side effects are possible. |
| Network or sandbox isolation | Blocks access to external services and mutable storage. | Use as a last-resort containment measure. |
Cancellation semantics matter. Some runners stop between steps but cannot interrupt a tool call that has already left the process. Others send a cancellation signal and wait for cooperative cleanup. Record whether the call was never started, interrupted, or completed; those states determine whether you must check the target system for a side effect.
How to roll back without creating a second incident
Rollback is not one operation. Separate the agent’s local state, your durable application state, and effects that occurred in systems outside your control.
Restore code and configuration
For source-controlled projects, identify the commit that passed your normal checks immediately before the run. Create a new recovery branch or snapshot, then revert or reset the affected branch according to your team’s change-control policy. Preserve the runaway branch and its diff for investigation rather than deleting it. Restore generated files only when they are reproducible; otherwise compare each changed file with the known-good snapshot and keep an explicit record of manual decisions.
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Configuration deserves the same treatment. Reapply the previous tool allow-list, model settings, prompt version, budget limits, network policy, and approval rules. A code rollback that leaves a permissive credential or tool policy in place simply recreates the exposure on the next run.
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Use the last checkpoint whose inputs, tool results, and state hash are known. If the runtime stores a transcript, restore the transcript and memory together; mixing a pre-incident prompt history with post-incident tool results can cause the agent to repeat actions or believe work is complete when it is not.
When no trusted checkpoint exists, start a new run from a clean state and supply only verified outputs. Mark the abandoned run as terminated, not successful, so downstream automation cannot treat it as a completed job.
Reconcile databases and files
Prefer an atomic database transaction or a versioned snapshot. If the loop performed several independent writes, determine which committed by querying durable audit records. Reverse only the writes that are safe to reverse, in dependency order. For files, compare hashes and timestamps, restore the approved snapshot, and quarantine unknown artifacts for review.
Do not assume that restoring local code or state reverses an external action. An email that was accepted, a purchase that was authorized, a deployment that reached a server, or a ticket created in another system remains real until that system confirms cancellation or a compensating action. Use idempotency keys to detect duplicates, issue documented compensating requests where supported, and obtain manual approval for refunds, deletions, or customer-facing messages.
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Protect against stale writes after recovery
A stopped worker can sometimes finish a request after you begin recovery. Require every write to carry the run ID or a version number and reject writes from runs that are no longer the active writer. OpenClaw documents this pattern with serialized per-session queues and an activeWriterRunId claim, preventing a superseded run from committing stale transcript data. The same principle applies to application records: compare the expected version in the write and fail on a mismatch.
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Controls that prevent runaway loops
Finite turns or iterations
Set a hard upper bound on model-tool cycles. OpenAI’s runner raises MaxTurnsExceededError when its turn limit is reached. Microsoft’s Agent Framework exposes max_iterations and states that autonomous loops should always be bounded. Choose a limit based on the largest legitimate task, then leave headroom for review rather than setting an effectively unlimited value.
A turn limit is a circuit breaker, not proof of success. When it trips, mark the run incomplete, preserve the trace, and require a human or a new controlled run to continue.
No-progress and work budgets
Count useful progress separately from turns. Cloudflare documents maxRecoveryWork as a runaway-loop guard and also provides a no-progress timeout and bounded out-of-memory retries. A useful progress signal can be a changed state hash, a newly satisfied checklist item, or a tool result that advances the task. Repeated calls with the same inputs should consume a retry budget quickly.
Set independent ceilings for wall-clock time, tokens, tool calls, memory recovery attempts, and monetary spend. Independent limits catch loops that stay under one threshold while exhausting another.
Explicit completion predicates
Define what must be true for the workflow to finish: required files exist and pass checks, a database record has the expected version, or an approval has been recorded. Microsoft’s loop APIs continue only while an evaluator returns true; returning false stops the loop. Treat a model statement such as done as advisory until the predicate and tool state agree.
Make the predicate observable. Log which condition is false on every iteration so an operator can distinguish legitimate remaining work from a condition that can never be satisfied.
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Cancellation and timeout behavior
Document what happens when a timeout occurs during each tool type. A read can usually be abandoned; a payment or deployment may need a status query before retrying. OpenClaw documents timeout aborts, while OpenAI’s lifecycle guidance covers cancellation and abort reconciliation. Implement a post-cancellation reconciliation step for any operation whose completion is uncertain.
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Allow only one active writer for a session or resource unless concurrent updates are explicitly designed. Queue serialization prevents two retries from applying the same change and makes the active run identifiable. Include a lease expiry so a crashed worker cannot hold the writer lock forever, and require a new run ID after takeover.
Approval gates and least privilege
Put destructive or expensive tools behind an approval gate. Issue short-lived credentials scoped to the exact resource and operation, and separate read and write identities. An agent that can inspect a production database does not automatically need permission to alter it. Approval records should include the run ID, requested action, target, approver, and timestamp.
Retry and handoff rules
Retry only errors classified as transient. Use exponential backoff with a finite retry count, and stop immediately on authentication, validation, or policy failures. Cap recursive handoffs by depth and total child work. Pass a structured parent ID to every child so an operator can terminate the whole tree rather than hunting individual runs.
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The following comparison uses documented behavior, not a claim that one runtime is universally safer. Evaluate the exact version and deployment you operate.
| Runtime or framework | Kill-switch ownership | Loop bound or work guard | Progress and completion behavior | Recovery or audit detail |
|---|---|---|---|---|
| OpenAI Agents SDK runner | The runner and its host application control cancellation; the model does not own the final stop. | A maximum-turn boundary raises MaxTurnsExceededError. |
Completion still depends on the runner’s termination logic and tool state. | Lifecycle guidance covers cancellation and reconciling an aborted run; preserve the trace in your host system. |
| Microsoft Agent Framework | The host controls the loop evaluator and can stop the run. | max_iterations provides a finite iteration ceiling. |
A predicate determines whether another iteration is allowed; Microsoft cautions that models and evaluators can stall or be probabilistic. | Expose evaluator results and iteration counts in your application logs. |
| Cloudflare Agents | The durable runtime and host controls provide the external boundary. | maxRecoveryWork, no-progress timeout, and bounded OOM retries limit recovery behavior. |
No-progress detection supplements, rather than replaces, a task completion condition. | Durable execution supports recovery, but you still need an application-level audit of side effects. |
| GitHub Copilot SDK | The CLI is the orchestrator of the tool-use loop. | Configure the session and host budgets; do not rely on a semantic task-complete message as a hard stop. | The documentation identifies session.idle as the reliable signal that processing ended, while task completion is a separate, less reliable semantic signal. |
Record session events, tool calls, and the final idle transition. |
| OpenClaw | The queue and session controller can abort a timed-out run. | Timeout aborts and serialized queues constrain concurrent work. | Per-session ownership prevents a superseded run from writing stale transcript data. | An activeWriterRunId claim provides a concrete stale-write check. |
Across these implementations, compare seven capabilities before choosing a runtime: who owns the kill switch; turn, iteration, and work limits; no-progress detection; cancellation during a tool call; checkpoint and stale-write protection; audit-trace quality; and behavior after a partial external side effect.
A practical guardrail configuration
Express limits as policy, not as instructions hidden in a prompt. A policy for a production workflow should specify:
- A maximum number of model-tool turns and child-agent depth.
- A wall-clock deadline, token ceiling, tool-call count, and spend ceiling.
- A no-progress threshold based on unchanged state or repeated arguments.
- An explicit completion predicate and the fields it checks.
- Which tools are read-only, which require approval, and which are prohibited.
- The credential scope and expiry time for every write-capable tool.
- The external cancellation endpoint and the escalation path to worker termination.
- The checkpoint or commit created before the run and the procedure for restoring it.
- The idempotency key and reconciliation method for each external API.
Log a policy version with every run. When a limit trips, emit a structured reason such as max_turns, no_progress, timeout, approval_denied, or stale_writer; avoid a generic failure message that hides the recovery path.
Restarting after containment
- Confirm isolation. Verify that the old run is cancelled or its worker is terminated, its queue is paused, and its credentials no longer authorize writes.
- Review the trace. Identify the first unexpected action, all completed external requests, and any uncertain in-flight calls. Check parent and child runs, not just the top-level ID.
- Restore the selected checkpoint. Apply the code, configuration, files, and database recovery plan. Keep a record of every object intentionally left unchanged.
- Reconcile external systems. Query request IDs and idempotency keys. Cancel, compensate, or manually review actions according to the target system’s rules.
- Run read-only verification. Exercise the completion predicate and inspect the would-be tool calls without executing writes. Confirm that stale run IDs are rejected.
- Restart narrowly. Use a fresh run ID, lower budgets, a limited dataset, and approvals for high-impact operations. Watch the first several iterations live.
- Close the incident. Preserve the final trace, root cause, policy changes, and evidence that the old run cannot write again.
Common failure modes and fixes
The cancel button reports success, but tools keep running
The control plane may have marked the run cancelled while a tool call was already in flight. Inspect worker status and the external target. Terminate the worker if it is still executing, then reconcile the tool request using its request or idempotency ID.
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The operation lacks an idempotency key or the key was regenerated on each attempt. Use a stable key derived from the business action and run scope, and query the target before issuing a compensating retry.
Rollback restores files but the application is still inconsistent
Local files are only one state store. Compare database versions, queues, caches, deployments, and third-party systems with the checkpoint. Restore or compensate each store explicitly.
The agent reaches the turn limit on every run
The limit may be masking an impossible completion predicate, a permission failure, or a tool that returns output without changing state. Inspect the first repeated state, correct the predicate or permission, and test with a read-only replay before increasing the limit.
Two runs overwrite each other’s recovery
Add a per-resource writer claim or optimistic version check. Reject commits from a run that is not the current owner, and serialize recovery operations until the active writer is unambiguous.
Logs cannot explain what happened
Capture immutable, structured events for prompts, model outputs, tool arguments and results, approvals, credentials or policy versions, timestamps, and external request IDs. A final transcript without these fields is not sufficient for duplicate-action analysis.
Incident checklist
- Writes frozen and read-only tracing preserved.
- Run, parent, and child identifiers recorded.
- External cancellation attempted and worker or sandbox status verified.
- Retries, recursive handoffs, credentials, and spend paths bounded.
- Prompts, model and tool versions, arguments, results, approvals, and timestamps exported.
- Known-good commit, checkpoint, snapshot, or transaction selected.
- External side effects listed with request and idempotency identifiers.
- Stale-write protection confirmed before restoration.
- Read-only replay passed and completion predicate explained.
- Restart performed with a fresh ID, lower budgets, and required approvals.
Microsoft’s Agent Framework summarizes the design requirement plainly: “Always bound autonomous loops. A completion condition can fail, a model can stall, and an evaluator can be probabilistic.” GitHub’s documentation identifies the control boundary just as clearly: “The CLI is the orchestrator of the tool-use loop.” Those principles, combined with an external kill switch and separate side-effect reconciliation, are what make stopping and rolling back an agent operationally safe.
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