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When an AI agent stalls, errors out, or repeats the same action, do not immediately press “retry.” First capture the run’s current state and last tool result, then check whether anything already changed. Classify the failure as transient, invalid input, an integration problem, a state/session failure, or an approval pause. Retry only suitable transient failures, with a limit and backoff; correct bad inputs and permissions; stop loops that produce no new evidence; and resume from the saved state or involve a person when a decision is required.
What do I do when my AI agent gets stuck?
Use this recovery sequence whether you are operating an agent in a consumer app or debugging one through an API. Product interfaces differ, so the labels may not match exactly, but the decisions are the same.
- Pause and capture what happened. Record the task you gave the agent, the visible error, the current run, session, or turn status, the last tool call and its result, and any output saved so far. In a developer system, also capture the structured error code, request identifier, session identifier, and turn identifier.
- Check for partial completion. Before replaying a command, inspect the target: a file, database record, calendar, message queue, ticket, or other external system. A failed turn can still have created a file, sent a request, or changed data. Verify the actual state and completed side effects.
- Classify the failure. Decide whether the problem is a temporary service issue, invalid input, missing credentials or permissions, a broken or unavailable tool, a failed session, repetitive behavior, or an intentional approval pause.
- Choose a bounded recovery. Correct invalid data or access settings; retry only appropriate transient failures; change the tool or instructions when the integration is wrong; or resume the existing run when it is paused or still healthy.
- Stop when there is no progress. Set a maximum number of attempts or a deadline. If the agent repeats an identical call without new information, interrupt it and inspect the latest state instead of allowing an infinite loop.
- Escalate with evidence. For a persistent failure, give a person or support team the IDs, timestamps, tool name, inputs, returned error, and whether an external side effect occurred.
This order matters. It prevents the most dangerous recovery mistake: sending the same side-effecting operation twice because the first response was lost.
How do I know whether an agent already completed the action?
A timeout or disconnected stream tells you that the response was not received; it does not prove that the operation did not run. Treat completion as an observable state, not as a property of the final message.
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Inspect the target system
Check the system the tool was meant to change. Look for the new file, record, email, deployment, payment authorization, ticket comment, or API event. Compare timestamps, unique request IDs, version numbers, and content hashes when those are available. If the target has an audit log, use it rather than relying on the agent’s summary.
Inspect run and turn status
Retrieve the latest run or turn status and any saved outputs. A run may be active, idle, paused for approval, completed, failed, or disconnected while the underlying session remains usable. OpenAI’s “Errors and recovery” guidance separates request, turn, session, and environment failures; use the status at the level that actually failed rather than assuming the whole session is lost.
Make replay safe
If you cannot establish whether the first call succeeded, do not blindly repeat a non-idempotent operation. Prefer a read-only lookup, an idempotency key, a “create if absent” operation, or a compare-and-swap update. If none is available, ask a person to decide after reviewing the target state. This is especially important for payments, messages, account changes, deletions, and production deployments.
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Repetition usually means the agent is not receiving useful new ground truth. The tool may return the same error, the result may be stale or empty, the agent may lack permission to make the requested change, or the instructions may reward calling a tool without a clear completion test. Google’s Gemini troubleshooting guidance identifies repetitive tool calling as a problem to diagnose rather than a reason to keep replaying calls. Anthropic’s engineering guidance states: “During execution, it’s crucial for the agents to gain “ground truth” from the environment at each step (such as tool call results or code execution) to assess its progress.”
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Break the loop
- Stop the current run at the configured iteration or retry limit.
- Read the newest tool output, not an earlier cached result.
- Check whether the tool arguments changed and whether the target state changed.
- Replace vague instructions such as “keep trying” with a completion condition and a failure condition.
- Provide the missing fact, permission, or alternative tool that would produce new evidence.
- Switch to a human decision when the next step depends on an ambiguous policy or sensitive action.
A retry is useful only when it can produce a different outcome. Repeating an identical request against an unchanged endpoint is not recovery.
How should I classify an agent failure?
| What you see | First response | Next step | Replay risk |
|---|---|---|---|
| Rate limit, timeout, temporary 5xx, or service unavailable | Check whether the operation completed; honor any server retry timing | Retry with exponential backoff, jitter, and a maximum count or deadline | Medium to high if the call had external effects |
| Malformed parameters, invalid request, credentials, permission, or billing error | Read the error details | Correct the field, credential, access, or configuration, then submit once | Usually low, but the corrected request may still have side effects |
| Tool error or unavailable endpoint | Inspect tool name, input, output, and integration health | Fix the tool or endpoint, or return a useful error to the agent | Depends on whether the tool ran before failing |
| Same action repeated with no new result | Stop at the loop limit | Change the approach, supply new information, use another tool, or ask a person | High if calls are not idempotent |
| Turn failure or stream disconnect | Retrieve the latest session, turn, and saved work | Check side effects and final status before resubmitting | High until completion is verified |
| Approval or safety pause | Treat it as an intentional stop | Approve, reject, or clarify, then resume the saved run | Controlled when resumed from the same state |
These categories are practical operating branches, not a universal error taxonomy. Individual products expose different codes and states.
When is it safe to retry?
Retry a call when the failure is plausibly transient, the operation’s completion state has been checked, and another attempt is allowed by a count or deadline. Honor a Retry-After header or equivalent server instruction. Use exponential backoff with jitter so many workers do not reconnect simultaneously.
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Temporary rate limits, network timeouts, and selected server errors are common retry candidates. Google’s current Gemini Python SDK documentation says its default behavior retries transient errors up to four times, starting with an approximately one-second delay and capping the delay at 60 seconds. Those values are SDK- and version-specific implementation details, not a general success rate or a rule for every language library. If you implement your own policy, choose a lower bound, a maximum delay, and a total deadline appropriate to the task.
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Failures that need correction first
Do not retry unchanged malformed parameters, expired credentials, missing permissions, invalid model or tool names, unsupported operations, or billing and quota configuration errors. A retry cannot repair an invalid request. Fix the identified cause and submit a new, validated request.
Side-effecting operations
For writes, sends, purchases, deletions, and deployments, combine retries with idempotency or a post-call read. If the API offers an idempotency key, reuse the same key for the logical operation rather than generating a new one for every attempt. If it does not, query for an existing result before creating another.
Should I restart an AI agent after an error?
Restart only the component that actually failed. A failed turn does not necessarily mean the session failed, and a disconnected stream does not necessarily mean the tool did not run.
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Continue the existing run when
- The session is still active or idle and its saved state is available.
- The failure was an approval pause that has now been resolved.
- The latest status shows unfinished work rather than a terminal session failure.
- You can identify the exact step that remains and have verified prior side effects.
Create a new session when
- The session itself is marked failed or cannot be retrieved.
- The cause has been corrected, such as a repaired integration or refreshed credential.
- The old context is corrupted, stale, or no longer appropriate for the task.
When starting over, carry forward a concise record of completed actions, verified target state, the error, and the remaining objective. Do not ask a fresh session to repeat an unverified write.
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What should developers build into an agent?
Define a clear tool contract
Document each tool’s purpose, required fields, allowed values, side effects, error shapes, and return schema. Anthropic’s tool-use documentation describes an application executing client-side tools and sending the result back in a tool_result; the model does not execute that application code by itself. Your application must therefore validate inputs, execute the call, and return a truthful, structured result.
Record observable state
Persist the request, run, session, and turn identifiers; tool calls and results; timestamps; status transitions; errors; approvals; and relevant saved work. Redact secrets and personal data from logs. This record lets an operator distinguish “the model asked for a call,” “the application executed it,” and “the target confirmed it.”
Bound every loop
Set a maximum retry count, maximum agent iterations, wall-clock deadline, and tool timeout. Return a structured stop reason when a limit is reached. Anthropic’s agent guidance describes maximum iterations as a control, while Google’s troubleshooting guidance recommends a maximum retry count. A limit should produce a diagnosable state, not silently discard the task.
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Let the transport layer handle narrowly defined transient errors. Let the agent decide what to do after a valid tool result. This prevents a model from turning a permission error or malformed request into an endless transport retry.
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Add human checkpoints
Require approval before sensitive tool calls, irreversible changes, or decisions involving ambiguous policy. Save the run state, present the proposed action and its parameters, and resume the same run after approval, rejection, or clarification. Do not convert an approval pause into an unexplained failure.
Design for safe replay
Use idempotency keys, unique operation IDs, transactional writes, and read-before-write checks where possible. Record whether a side effect was confirmed. If confirmation is unknown, expose that uncertainty to the operator instead of reporting a confident failure.
What can a consumer user do when the interface is limited?
Consumer apps may not expose request IDs, turn status, or resumable sessions. Translate the same diagnostic method into visible actions:
- Stop the current generation or tool activity if the interface allows it.
- Read and copy the exact error message and note the time.
- Check the destination: files, messages, settings, orders, or other changed content.
- Correct the prompt, attachment, account permission, or connected-app access identified by the error.
- Resume only the unfinished part, stating what has already been completed.
- If the agent repeats itself, start a support request with the error text, screenshots, task description, and whether the target changed.
Do not assume that closing the app cancels an external action. Verify the destination first, especially for messages, purchases, account changes, and deletions.
When should I stop retrying and ask a person?
Escalate when the retry limit or deadline is reached, the same result repeats without new evidence, the target state cannot be determined, an operation is sensitive or irreversible, permissions require an owner, or the next step depends on a policy judgment. A person should receive enough context to decide without replaying the incident.
- Task and intended outcome
- Request, session, and turn IDs, if available
- Timestamp and time zone
- Last tool name, sanitized inputs, and complete returned error
- Run status and number of attempts
- Verified target state and any external side effect
- What has already been tried
For persistent API failures, OpenAI’s recovery guidance specifically recommends including request, session, and turn identifiers. For integrations, preserve the tool name, input, output, timestamp, and side-effect status.
A compact decision checklist
- State: What is the current run, session, or turn status?
- Evidence: What was the last tool result or visible error?
- Side effects: Did the target system change?
- Cause: Is this transient, invalid input, access, integration, state, repetition, or approval?
- Progress: Did the last attempt produce new information?
- Limits: How many retries or iterations remain, and what is the deadline?
- Next action: Correct, back off and retry, change tools, resume, or escalate?
The safest recovery is the one that uses new evidence, changes only what failed, and has a defined stopping point.
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