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Build recovery into the workflow, not just into a retry setting. A robust multi-step AI agent classifies failures, retries only temporary ones within a fixed budget, saves enough progress to resume after interruption, and checks uncertain external actions before repeating them. If recovery cannot proceed safely, the workflow should route to a fallback, compensation step, human review, or clear terminal failure.
Why a failed agent run does not tell you what happened
A multi-step agent can call a model, invoke tools, update workflow state, and change external systems such as files, calendars, or payment services. A timeout or process crash may occur after an action succeeds but before the workflow records the result. The agent’s visible failure therefore does not prove that every earlier step failed.
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OpenAI’s official “Errors and recovery” guide warns: “A failed turn may already have changed files or called external tools.” That is the core safety problem. Blindly replaying the whole task can duplicate an email, repeat a file change, or submit an action twice. Conversely, assuming that an uncertain action succeeded can leave the task incomplete.
Recovery is a workflow property. For each step, decide what errors mean, whether another attempt is safe, what state must survive interruption, how external effects will be reconciled, and what happens when automatic recovery stops. These choices apply whether the implementation uses an agent SDK, a graph framework, or a durable workflow engine.
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Start by defining what each step can fail like
Before adding retry logic, list the workflow steps and classify their likely failure modes. The same policy should not be applied to every error: a temporary service overload and an invalid request require different responses.
| Failure class | Typical examples | Recovery decision |
|---|---|---|
| Transient service condition | Temporary overload or timeout | Retry only if the operation is safe to retry, honoring a server-provided delay where applicable and staying within an attempt or time limit. |
| Deterministic request or configuration problem | Invalid input, unsupported configuration, or a malformed request | Correct the request or configuration before submitting again; an unchanged retry is unlikely to help. |
| Credential or permission problem | Invalid credentials or insufficient access | Resolve the credential or permission cause before resuming the affected step. |
| Unexpected model or tool result | Malformed output or behavior outside the expected format | Validate the result, capture the failure context, and route to a repair, fallback, or human-review path instead of treating every bad result as a temporary outage. |
| Ambiguous external effect | A timeout after a request that may have changed an external system | Reconcile with the system of record before deciding whether to repeat the action. |
These categories are a practical design framework, not a guarantee that every service labels errors identically. OpenAI’s guidance recommends using structured error information and request parameters to diagnose failures; its Agents SDK documentation distinguishes model timeouts, tool timeouts, and unexpected model behavior. Use the actual error details exposed by the model, tool, and runtime rather than inferring the cause from a generic “failed” status.
Design a bounded retry policy for each step
A retry is a new attempt at an operation. It should have a clear reason to exist and a stopping rule. Retrying a temporary overload may work; resubmitting invalid input unchanged generally will not. Retrying an action whose prior result is uncertain can be dangerous even if the underlying service error looks transient.
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Make retry eligibility explicit, ideally by error class or step-specific policy. Treat validation failures, unsupported settings, and credential problems as conditions to correct, not as invitations to repeat the same call. For timeouts, determine whether the timed-out operation is read-only, safely repeatable, or capable of creating an external effect.
Set both a cap and a time boundary
Limit attempts and, where appropriate, the total time spent recovering. An attempt cap prevents an endlessly failing step from cycling forever; an overall deadline prevents a run from consuming resources long after it can still meet its purpose. The exact values depend on the service, task, and user expectation; the reviewed documentation does not establish one universal retry count or deadline.
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Honor service guidance and stop when the situation changes
When a service provides a Retry-After delay, OpenAI recommends honoring it. Use backoff rather than sending repeated requests immediately when a transient condition calls for waiting. Stop when the retry limit is reached or the error changes into a condition that is not eligible for automatic retry. A changed error can indicate that continuing with the same policy is no longer appropriate.
Apply policy at the step boundary
Different steps have different risks. A read operation, a model call, and a payment submission should not automatically inherit one identical retry policy. LangGraph documents node-level retry policies and timeouts, allowing controls to be applied around individual graph steps. Treat that as a capability, not a reason to retry every node: the policy still needs to match the operation’s semantics.
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A retry repeats an attempt; a resume continues a persisted workflow from known progress. To resume safely after a process restart, persist enough state to tell which steps completed, what data they used and produced, and what work remains. A fresh process should not have to guess whether an earlier step ran.
Save state at meaningful boundaries
Record progress after meaningful workflow steps, especially before or after calls that may take time or cause external effects. LangGraph describes checkpointers as snapshots of graph state. OpenAI’s Agents SDK documentation identifies durable-execution integrations for work that spans waits, retries, or process restarts. These mechanisms can preserve workflow progress, but they do not remove the application’s responsibility to record information that lives outside the workflow state.
Make recovery state useful, not merely present
A checkpoint should help the workflow determine what to do next. At minimum, the recovery design needs to identify the run and step, retain the input and validated output needed for continuation, record the attempt and failure context, and distinguish a completed step from one that is pending or uncertain. Keep external action status separate from ordinary agent reasoning or intermediate text: “the agent intended to send” is not the same as “the message was sent.”
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Choose persistence boundaries based on failure exposure. If a crash between two steps would otherwise force a consequential action to be repeated, save enough progress around that boundary to support reconciliation. A checkpoint is a record of workflow state, not proof that a remote service accepted a request.
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Reconcile side effects before replaying a step
When an action may already have happened, inspect the saved workflow state and the external system before asking the agent to repeat it. The system of record—such as the relevant file store or service—must establish whether the effect occurred. The agent’s generated explanation or local checkpoint alone may not settle that question.
Use an action ledger as an application-level design
A practical design inference from OpenAI’s recovery guidance is to keep an application-level record of external actions. For each consequential action, record the intended operation and enough identifying information to look up its result, then update the record when the outcome is confirmed. If a timeout leaves the result uncertain, query the external system or inspect its state before choosing whether to retry, continue, or ask a person.
This ledger is not a universal feature prescribed by the reviewed documentation; its shape depends on the application and external service. The important behavior is to distinguish “not attempted,” “attempted with confirmed success,” “attempted with confirmed failure,” and “outcome unknown.” Do not convert “unknown” into “failed” merely because the client stopped waiting.
Use idempotency where the external API supports it
If the external service supports idempotency keys, a stable key for the same logical action can help prevent duplicate effects when a request is retried. That mechanism is service-specific: verify what the API promises and how long it recognizes a key. If idempotency is unavailable, query the external system or require human confirmation before replaying an ambiguous consequential action.
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Idempotency does not replace workflow state or reconciliation. It can reduce duplicate risk for a supported request, but it cannot tell the agent whether a different step completed, whether a request used the right inputs, or whether the external system reached the intended final state.
Route exhausted and non-retryable failures deliberately
Automatic recovery needs an explicit destination. If an error is deterministic, retries are exhausted, or an outcome cannot be safely established, do not leave the run in an ambiguous loop. Route it to a handler that can repair, compensate, request review, or mark the task as failed with a useful explanation.
Choose the right terminal path
- Repair: Correct a known input or configuration problem, then resume only the affected unfinished work.
- Fallback: Use an alternate supported path when it can fulfill the task without silently changing the requested result.
- Compensation: Where a completed action must be counteracted, run a deliberate compensating step and track whether that step succeeds. LangGraph documents error handlers that receive failure context and can use routing commands for compensation patterns.
- Human review: Escalate when the outcome is uncertain, consequential, or outside the automation’s safe decision boundary.
- Terminal failure: Stop with the failed step, known completed work, and remaining uncertainty clearly represented.
A handler should receive enough failure context to make its decision: which step failed, what error was observed, which attempt was made, what state was checkpointed, and whether any external effect is confirmed. After a handler resolves the issue, continue only work that is unfinished; do not replay already completed steps merely to make the run appear linear.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Model human approval as pause and resume
Approval is a separate workflow branch, not a failed attempt. When an action needs human review, save the run state, present the decision and relevant context, pause execution, and resume from that saved state after approval or rejection. OpenAI describes durable agent workflows that include human-in-the-loop tasks. LangGraph documents interrupts for human review and notes that interrupts bypass retry policies and error handlers.
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Log enough to explain and operate recovery
Recovery is difficult to debug if logs say only that “the agent failed.” Record structured metadata for each meaningful attempt so operators can reconstruct the sequence without relying on generated narrative.
- Run or session identifier and step name.
- Attempt number, timeout or deadline context, and the checkpoint identifier when available.
- Error class and structured error details, while avoiding exposure of secrets or sensitive data.
- Whether the step’s external effect is confirmed, failed, or still uncertain.
- Which recovery route was selected: retry, resume, handler, compensation, human pause, or terminal failure.
LangGraph exposes execution metadata such as attempt number, checkpoint ID, run ID, and task ID. Use comparable identifiers from the chosen runtime when available. Also set overall turn, loop, or cost limits appropriate to the application. The reviewed sources identify an Agents SDK maximum-turn exception, but do not establish a universal token or cost budget; choose those limits from your own product requirements and operational constraints.
A practical recovery sequence when a step fails
- Capture the failure. Save the structured error, failed step, attempt number, and run identifier. Determine whether the workflow is still active or has stopped.
- Load the durable state. Retrieve the saved session or checkpoint and identify completed, pending, and uncertain steps.
- Inspect external actions. For every action that may have changed an external system, check the system of record or action ledger before replaying it.
- Classify the cause. Decide whether it is transient, deterministic, credential-related, unexpected output, or an uncertain external effect.
- Choose a safe route. Correct deterministic causes; for eligible transient failures, wait according to service guidance and retry within the configured budget. Reconcile ambiguous side effects before retrying.
- Stop or escalate when needed. If the limit is reached or safe continuation is not possible, route to a fallback, compensation handler, human review, or explicit terminal failure.
- Resume only unfinished work. Continue from persisted progress after recovery, keeping confirmed completed actions out of the replay path.
This sequence combines error diagnosis, bounded retry, checkpointing, error handling, and interrupt mechanisms documented by OpenAI and LangGraph. It is a workflow design pattern, not a claim that any framework automatically reconciles every external side effect.
Choosing a recovery boundary: graph framework or durable orchestration
The documented approaches differ in where they place the recovery boundary. A graph framework can provide persistence and fault-tolerance features around graph execution. A durable workflow engine or integration can support longer-running orchestration across waits and restarts. These are capabilities described by the respective documentation, not a reliability ranking or a guarantee that every feature is equivalent.
| Approach | Documented recovery mechanisms | What the application still needs to decide |
|---|---|---|
| LangGraph graph execution | Node retries, timeouts, error handlers, checkpointed graph state, and human interrupts. | Which failures are retryable, how external effects are reconciled, and what handlers do for the application’s failure cases. |
| OpenAI Agents SDK with a durable-execution integration | The SDK documentation lists integrations with Dapr, Temporal, Restate, and DBOS for durable or long-running workflows, including work across waits, retries, or process restarts. | Which integration fits the existing architecture and what application state and external actions must be recorded explicitly. |
| Temporal workflow examples | Temporal’s AI Cookbook index lists examples for durable agents, human approval, retry policies, and multi-agent workflows. | How to adapt examples to the application’s workflow semantics, external APIs, and operational requirements. |
Before selecting an approach, check its current documentation and version requirements. LangGraph’s fault-tolerance documentation notes version requirements for some features. The cited material establishes technical capabilities and integration examples, but does not compare operational costs, guarantee feature parity, or provide comparative reliability benchmarks. Consider compatibility with the current framework, required persistence and runtime components, how long work may wait for a person, and which team will operate the recovery path.
Implementation checklist
- Every step has a failure classification and a defined retry eligibility rule.
- Retry policies have an attempt cap or deadline, use backoff where appropriate, and honor server-provided retry delays.
- Invalid inputs, credentials, and configuration errors are corrected rather than retried unchanged.
- Workflow progress and failure context are persisted at useful recovery boundaries.
- External effects can be checked before replay; supported idempotency mechanisms are used deliberately.
- Retry exhaustion and non-retryable failures route to a handler, fallback, compensation, human review, or explicit failed state.
- Human approval pauses and resumes from saved state instead of entering ordinary retry handling.
- Logs identify the run, step, attempt, checkpoint, error class, and external-effect status.
- Overall turn, loop, and cost limits prevent recovery from becoming unbounded.
Sources and scope
This design guide reflects mechanisms described in OpenAI’s Agents SDK “Running agents” and “Errors and recovery” documentation, LangChain/LangGraph’s “Fault tolerance” documentation, and Temporal’s “AI Cookbook” index. Those sources document implementation capabilities rather than independent reliability tests. Their cited material does not establish a universal retry count, cost budget, or benchmark for one approach against another.
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