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To recover a multi-step AI agent safely, persist its progress, classify each failure before retrying, and check whether an external action already happened before replaying it. Give each step its own bounded retry and timeout policy, then define what happens when recovery is not safe or retries run out. A retry repeats an attempt; a resume continues from saved progress. That distinction is central to recovering after a timeout, tool error, or process restart without repeating completed work.
Why a failed agent run needs more than a retry
A multi-step agent can call tools, modify files, wait for a service, and use the result of one step to decide what to do next. If a run fails halfway through, its error does not tell you whether earlier steps completed or whether an external action took effect. OpenAI’s recovery guidance puts the risk plainly: “A failed turn may already have changed files or called external tools.”
Blindly restarting the whole run can duplicate an email, file change, or other action. Conversely, treating every error as terminal can abandon work that could have continued after a temporary outage. Recovery therefore needs to account for both workflow state and effects outside the workflow.
The mechanisms documented by OpenAI’s Agents SDK, LangGraph, and Temporal support different parts of this design. LangGraph documents graph checkpoints, node-level retries and timeouts, error handlers, and human interrupts. OpenAI’s Agents SDK describes exception categories and integrations for durable execution. Temporal’s AI Cookbook lists examples covering durable agents, retry policies, and human approval. These are documented capabilities, not evidence that one framework is more reliable or less costly than another.
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Start by classifying the failure
Before deciding to retry, identify what failed and whether the same request could succeed if attempted again. OpenAI’s error guidance recommends using structured error information and request parameters to diagnose failures. Its Agents SDK distinguishes model timeouts, tool timeouts, unexpected model behavior, and a maximum-turn exception. That classification is more useful than treating every exception as the same kind of failure.
| Failure class | Typical response | Why |
|---|---|---|
| Transient service condition, such as overload or a timeout | Wait as directed, then retry within a defined attempt or time budget. | The underlying condition may clear, but an unbounded loop can waste resources or stall the run. |
| Invalid input or unsupported configuration | Correct the request or configuration before submitting again. | Repeating the same invalid request is unlikely to change the result. |
| Credential or authorization problem | Resolve the credential or permission issue before continuing. | An unchanged authorization failure is not a transient retry case. |
| Malformed or unexpected result | Validate the result, then route to a correction, fallback, or review path appropriate to the step. | The workflow should not assume that an unusable result is safe to pass to later steps. |
| Uncertain outcome of an external action | Check the system of record before deciding whether to replay the action. | A timeout can occur after the action took effect but before the workflow received confirmation. |
These response categories are a design application of the error distinctions and recovery mechanisms in OpenAI’s documentation; they are not a universal mapping of every provider’s error codes. Preserve the actual structured error and the relevant request context so an operator or handler can tell whether the cause is transient, correctable, or ambiguous.
Design a bounded retry policy for each step
Retry only failures that are plausibly transient. Follow a server-provided delay such as Retry-After when applicable, and set an attempt cap or deadline so the workflow cannot retry forever. OpenAI’s recovery guidance recommends honoring retry instructions, limiting retries, and stopping automatic retries when the error changes or the limit is reached.
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Apply policy at the smallest useful recovery boundary. A model call, a tool call, and a validation step can fail for different reasons; one global retry setting may repeat too much work or retry the wrong class of failure. LangGraph documents node-level retry policies and timeouts, allowing policy to be associated with individual steps. This is a useful design pattern whether the implementation uses a graph framework, an agent SDK, or a workflow engine.
Decide what is retryable
- List the failure classes each step can produce, including timeout and invalid-result cases.
- Specify which transient errors may be retried and which deterministic problems must be fixed first.
- Define what evidence changes the decision. For example, a different error after a retry may indicate that the original failure is no longer the relevant problem.
Set both attempt and time limits
An attempt limit bounds repeated calls; a deadline bounds how long the workflow spends waiting or retrying. Use the limits that fit the step’s purpose and the overall run, rather than assuming that retries should continue until success. The documentation describes per-step retry and timeout mechanisms, but does not establish a universally correct number of attempts or a standard token or cost budget. Choose those limits for your application and make them observable.
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Keep retry decisions separate from correction
If a request has invalid input, bad credentials, or unsupported configuration, route it to a correction path instead of resubmitting it unchanged. If a result is malformed, validate it before downstream steps consume it. A retry is an attempt to overcome a recoverable condition; it is not a substitute for repairing a deterministic cause.
Persist enough progress to resume safely
A durable workflow needs a record of where it was, what each relevant step received and returned, and which work is still unfinished. LangGraph checkpointers save snapshots of graph state. OpenAI’s Agents SDK documentation describes durable execution integrations for workflows that span waits, retries, or process restarts. Temporal’s cookbook includes durable-agent examples. These approaches provide mechanisms for preserving workflow progress, but persistence of workflow state does not by itself establish that an external action happened.
At each recovery boundary, make sure the saved state is sufficient to continue without guessing. Depending on the application, this may include the run or session identifier, the current step, validated outputs needed by later steps, retry context, and a reference to any action whose outcome needs reconciliation. Treat that list as an application design choice, not a required schema prescribed by the cited frameworks.
Separate retry from resume
A retry repeats an attempt at a step that did not produce an acceptable result. A resume continues a persisted workflow from its known progress after an interruption. A timeout at one step may call for a retry of that step; a process restart after several completed steps generally calls for resuming from saved state, not replaying the entire task. This distinction follows from the difference between documented retry policies and checkpoint/resume mechanisms.
Track workflow state and external effects separately
A checkpoint can show that a workflow reached a point in its graph, but the application may still need to determine whether a payment, email, file change, or other external action actually occurred. OpenAI explicitly instructs users to inspect session state and saved work, then confirm completed actions before asking an agent to repeat them.
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A practical application-level pattern is to record an action’s intent and outcome in a ledger, use an idempotency key if the external service supports one, or query the external system before replaying an action. The cited documentation establishes the need to inspect completed effects; it does not prescribe a single ledger format or idempotency implementation. If the external service provides no reliable way to determine whether an action took effect, treat the outcome as ambiguous and route it for a safe decision rather than assuming success or failure.
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Use this recovery sequence when a step fails
- Capture the failure context. Save the structured error, step name, attempt number, and run or session identifier. Determine whether the workflow is still active or has stopped.
- Load persisted progress. Retrieve the latest session state or graph checkpoint and identify which steps completed and which outputs are available to continue.
- Inspect actions already taken. For each external effect that could have occurred, check saved work and the relevant system of record. Do not infer that an action failed just because its response timed out.
- Classify the error. Decide whether it is transient, deterministic, malformed-output, or an uncertain external outcome. Preserve the error details used to make that decision.
- Choose a recovery path. For a transient error, observe the server’s retry guidance and retry within the step’s remaining budget. For invalid input, credentials, or configuration, correct the cause. For an uncertain effect, reconcile before replaying. For an unusable result, validate and use the appropriate correction or fallback path.
- Continue only unfinished work. Resume from known progress after recovery; do not rerun completed steps unless the workflow has established that doing so is safe.
- Escalate when recovery is exhausted or unsafe. Route to a fallback, compensation handler, human review, or explicit terminal failure. Record the resulting status so the run is not left in an ambiguous state.
This sequence combines OpenAI’s recovery instructions with the retry, timeout, checkpoint, handler, and interrupt mechanisms documented by LangGraph. Exact APIs and behavior vary by framework and version; consult the documentation for the version in use before implementing a specific configuration.
Define what happens when automatic recovery ends
A recovery policy is incomplete if it specifies retries but not what happens when they are exhausted. LangGraph documents error handlers that receive failure context and can use routing commands for compensation patterns. At the application level, choose an explicit destination for a non-retryable or exhausted failure.
- Fallback: Continue using an alternate path only when its output is acceptable for the task. A fallback should not silently turn an invalid or uncertain result into a trusted one.
- Compensation: If earlier work needs to be counteracted, run a deliberate compensation step and record its outcome. Compensation is not the same as rolling back an external action automatically; the application must define what is reversible and how to verify it.
- Human review: Pause when the action is consequential, ambiguous, or not safely reversible by an automated handler.
- Terminal failure: Stop with a clear status and enough context to explain what failed, what completed, and what still needs attention.
Do not let a handler hide the original failure. Preserve the triggering error alongside the recovery result so operators can distinguish a successful fallback or compensation from a task that simply stopped retrying.
Model human approval as pause and resume
When a decision needs human approval, represent it as a distinct pause/resume path rather than another failed attempt. OpenAI describes durable agent workflows that can include human-in-the-loop tasks. LangGraph documents interrupts for human review and notes that interrupts bypass retry policies and error handlers. That behavior makes an interrupt different from an exception to be retried or handled like a normal node failure.
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Persist the workflow state needed to continue while approval is pending. When the reviewer responds, resume from that saved state and proceed only with work that remains. For an action with an uncertain external outcome, approval should be based on the reconciled state where possible; asking for approval to repeat an action without checking whether it already happened can preserve the original risk rather than resolve it.
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Record enough metadata to reconstruct the run’s path and explain each recovery decision. LangGraph exposes execution metadata including attempt number, checkpoint ID, run ID, and task ID. An application can use corresponding identifiers and fields in its own logs, even when it does not use LangGraph.
- Run or session identifier and the step name.
- Attempt number and the checkpoint or saved-state reference.
- Error class and structured error details.
- Whether an external effect was attempted, confirmed, or remains uncertain.
- The selected recovery route: retry, correction, resume, fallback, compensation, human pause, or terminal failure.
Use the logs to distinguish a step that never ran from one that ran without returning a confirmation. Set whole-run limits alongside per-step policies: an otherwise bounded retry loop can still be embedded in an unbounded agent turn or workflow. OpenAI’s Agents SDK documentation identifies a maximum-turn exception, but the reviewed documentation does not define a universal token or cost budget. Select and enforce overall limits for the needs of your application.
Choose a recovery boundary that fits the workflow
Graph-framework persistence and durable workflow orchestration address related but not identical boundaries. The appropriate choice depends on where the workflow needs to recover, what state must survive, and which components the application already uses. The documented feature lists below are not a feature-parity assessment, operational-cost comparison, or reliability ranking.
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|---|---|---|
| LangGraph graph execution | Node-level retries and timeouts, error handlers, checkpointed graph state, and human interrupts. | Which graph state is checkpointed? How are external effects verified? Which documented features are available in the framework version you deploy? |
| OpenAI Agents SDK with a durable-execution integration | The SDK documentation describes integrations for Dapr, Temporal, Restate, and DBOS for durable or long-running workflows; it also documents distinct exception cases. | Which integration fits the existing runtime and workflow boundary? What application data or external effects need explicit recording? What components and operational responsibilities does that integration require? |
| Temporal workflow patterns | The AI Cookbook index lists examples for durable agents, retries, human approval, and multi-agent workflows. | How will the workflow represent its steps, approval pauses, retry policies, and external-action reconciliation in the chosen implementation? |
The sources establish that these mechanisms and examples are documented. They do not establish equal feature coverage, a universal database requirement, identical integration behavior, or comparative operational costs. Check the current version-specific documentation and the requirements of the integration you select.
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Common recovery design mistakes
Retrying every exception
This can repeatedly submit invalid requests or failed authorization attempts without changing the cause. Classify the error first and reserve automatic retries for errors the policy considers transient.
Restarting the whole agent after a partial failure
A restart can replay completed work. Load saved progress, inspect external actions, and continue from the appropriate recovery boundary instead of treating the task as if nothing happened.
Assuming a timeout means an action did not happen
A timeout may mean only that confirmation was not received. Reconcile against the external system before deciding whether to replay the action.
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Persisting a checkpoint but not recording external outcomes
Workflow state and side-effect status answer different questions. Save enough application-level information to verify external outcomes, especially for actions that cannot be safely repeated.
Treating human review as another retry
An interrupt is a deliberate pause in execution. Model how state is retained and how work resumes after review instead of sending the pause through a retry loop.
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