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Keep an AI workflow paused when it is waiting for approval or required input; resume it once that decision is ready. Retry only after you understand the failure, confirm that the platform’s retry policy permits it, and account for any external action that might already have happened. “Retry,” “resume,” and “restart” are not interchangeable across platforms.
Choose the action by asking why the workflow stopped
First check the run’s status, the step that stopped, and whether the stop was expected. A request for human approval is a pause, not necessarily a failure. A runtime error, validation problem, or failed business rule may require a different response. If you cannot tell which occurred, hold the run while you inspect it—particularly if another attempt might send a message, create a record, charge a payment, or otherwise affect an external system.
- Keep holding if the workflow is waiting for an approval or input that has not arrived, or if the failure and its possible side effects are still unclear.
- Resume when the expected approval or input is ready and the platform can continue using the intended saved state or checkpoint.
- Retry when the failure is understood and retryable under the platform’s policy, and any side effect that could be repeated is safe or has been reconciled.
Before acting, verify what the platform means by its control label and which workflow definition and saved data the action will use. Product behavior varies; these are decision principles, not a universal sequence of buttons.
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Resume generally means continuing with runtime state or a checkpoint, rather than starting a fresh run. But a checkpoint does not guarantee that every in-progress operation is protected from repetition. The key question is where the platform draws its replay boundary: completed work may be restored while unfinished work runs again.
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OpenAI Agents SDK
The OpenAI Agents SDK guide treats human approvals as paused runs. It advises resolving the interruption and resuming from saved state, rather than starting a new user turn. That approach preserves turn history and server-managed continuation IDs. If a stream is still running, wait for it to finish before treating the run as settled; if a stream was cancelled but the same turn should continue, the guide says it can be resumed from state.
LangGraph Functional API
In the LangGraph Functional API, resuming returns execution to a checkpoint boundary. The entrypoint runs again, while completed task and subgraph results are restored from the checkpointer. A task that started but did not finish may run again. As the documentation puts it, “When you resume a workflow run, the code does NOT resume from the same line of code where execution stopped.” Checkpointed inputs, outputs, and task results must be JSON-serializable.
For operations that can affect another system, make repeated execution safe where possible: use an idempotency key, or check whether the intended result already exists before creating it. If an operation’s outcome is uncertain, verify the external system before resuming rather than assuming the checkpoint tells you whether the side effect completed.
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Temporal distinguishes retrying an Activity Task from retrying a Workflow Execution. Heartbeat payloads can carry progress across Activity Task retries, allowing a long-running Activity to continue from a checkpoint. That is a Temporal-specific mechanism; do not assume another framework uses the same boundary or preserves the same progress.
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How retries differ across platforms
A retry policy applies to particular failure classes, not necessarily every run that appears failed. Identify the failing unit and read the configured policy before manually starting another attempt.
Temporal: task failure versus execution failure
Temporal automatically retries Workflow Task failures while the Workflow Execution remains open. A Workflow Execution failure closes with failed status and retries only when a Workflow Retry Policy is configured. Each execution retry is a separate run with its own event history. These distinctions explain why “retry the workflow” can mean something different from the service’s automatic retry of a task.
LangGraph: node policies and interrupts
The LangGraph fault-tolerance guide describes per-node retry policies and error handlers whose behavior depends on the error type and configured policy. Interrupts bypass those policies and handlers because they pause the graph for human-in-the-loop work. Its graceful drain feature saves a resumable checkpoint between supersteps; the documented drain feature requires LangGraph 1.2 or later in Python, and resumption uses the same thread ID. These specifics apply to the documented LangGraph behavior and version, not to workflow systems generally.
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n8n: choose which workflow definition to retry
The n8n executions documentation describes retrying a failed workflow using previous execution data and choosing either the currently saved workflow or the original workflow. If you edited the workflow after the failed execution, that choice determines which definition is used. Check the current interface and deployment-tier availability before relying on a particular retry option.
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A safe recovery checklist
- Read the run status and failure point. Decide whether this is an expected approval or input pause, a runtime or validation error, or a business-logic failure.
- Check for uncertain side effects. Look in the external system—such as the message history, record store, or payment system—if the stopped step may have acted before failing.
- Confirm the continuation state. Identify the run, thread, checkpoint, or execution data that resume or retry will use. For edited workflows, confirm whether the original or current definition will run.
- Check retry rules. Review which failures are retried automatically, whether a policy is configured, and whether a manual retry is appropriate for this failure class.
- Make repetition safe, then act. Use idempotency or duplicate detection where available; reconcile any uncertain result. Resume an expected pause with its intended state, or retry only when the failure and consequences are understood.
- Watch the next execution. Confirm that it reaches the expected state and that external effects occurred once, rather than assuming a successful resume or retry guarantees that outcome.
What to compare when choosing a workflow platform
Recovery documentation is not a reliability benchmark, and the platforms below expose different parts of the problem rather than a single comparable score. When assessing a system for long-running AI workflows, examine these operational questions:
- Pause type: Can the system distinguish an expected approval or input wait from runtime, validation, and business-logic failures?
- State continuity: Does continuation use the same run, thread, checkpoint, or a new run?
- Replay boundary: Which completed results are restored, and which unfinished steps may execute again?
- Retry controls: Which failures are retried automatically, and how are policies and error classes configured?
- Side-effect safety: Can integrations use idempotency keys, detect duplicates, or reconcile uncertain outcomes?
- Operator visibility: Can you inspect execution history and determine which workflow definition a retry will use?
The official documentation establishes specific runtime behavior, not which platform is best overall or how every deployment is configured. Check the documentation for the framework version and deployment you actually use before taking an irreversible recovery action.
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