Keep an AI workflow paused when it is waiting for approval or required input; resume it when that input is ready. Retry only after a genuine failure is understood, the platform’s retry policy permits it, and you have checked that repeating any external action will not cause harm. “Retry,” “resume,” and “restart” can mean different things across platforms, so check the run status, saved state, and recovery behavior before acting.
First identify why the workflow stopped
A run that is waiting for a person is not necessarily broken. An approval request or missing input is an expected pause: the workflow needs a decision or information before it can proceed. A runtime error, validation error, or failed business rule is different; the run may need correction or a retry, depending on the cause and the platform.
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When the state is unclear, keep holding while you inspect it. In particular, do not assume a failed-looking run had no effect: a step might have sent a message, created a record, or otherwise changed an external system before the error appeared.
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| Situation | Best next action | Check before acting |
|---|---|---|
| Waiting for approval or required input | Keep holding until the decision or input is ready, then resume from the saved state. | Confirm the run is paused for that reason and that you are continuing the intended run. |
| Failure with an unclear cause or uncertain external effect | Keep holding while you inspect the error and reconcile any possible side effect. | Check execution history, external records, and whether the action already succeeded. |
| Known, retryable failure with duplicate effects prevented or reconciled | Retry under the platform’s configured policy. | Confirm what version of the workflow and what prior execution data the retry will use. |
| Interrupted run that should continue from its saved state | Resume using the relevant state or checkpoint. | Find out which completed work is restored and which unfinished work may run again. |
This is a decision guide, not a universal command sequence. A product’s “retry,” “resume,” “continue,” or “restart” control may have platform-specific semantics.
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What resume preserves—and what it may run again
Resume generally means continuing from runtime state or a checkpoint rather than starting a fresh run. That does not guarantee that execution picks up at the exact line or step where it stopped. The replay boundary matters: some systems restore completed task results while re-executing work that began but did not finish.
OpenAI Agents SDK
The OpenAI Agents SDK guide treats human approvals as paused runs rather than new turns. It advises resolving the interruption and resuming from saved state, preserving turn history and server-managed continuation IDs. It also advises waiting for a stream to finish before treating the run as settled; if a stream is cancelled but the same turn should continue, the guide describes resuming from state.
LangGraph Functional API
In the LangGraph Functional API, resume returns execution to a checkpoint boundary. Replay starts at the entrypoint; completed task and subgraph results are restored from the checkpointer, but a task that started and did not finish may run again. Inputs, outputs, and task results must be JSON-serializable for checkpointing and resumption.
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For this reason, make side-effecting operations safe to repeat. LangGraph recommends idempotency keys or checking whether a result already exists. The same documentation says, “When you resume a workflow run, the code does NOT resume from the same line of code where execution stopped.” This describes that API’s replay behavior; it is not a rule for every workflow system.
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LangGraph fault tolerance
LangGraph’s fault-tolerance documentation describes configurable per-node retry policies and error handlers. 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, requires LangGraph 1.2 or later in Python, and resumes using the same thread ID. These details are version- and framework-specific.
When retry is appropriate
Retry is for work that failed, not a substitute for answering an approval request. Before retrying, classify the failure, read the platform’s configured policy, and determine whether the step could already have completed an external action. If its outcome is uncertain, verify the external state or use a deduplication mechanism before repeating the operation.
Temporal
Temporal distinguishes Workflow Task failures from Workflow Execution failures. The service automatically retries Workflow Task failures while the Workflow Execution remains open. A Workflow Execution failure closes with failed status and is retried only when a Workflow Retry Policy is configured; each retry is a separate run with its own event history. For long-running Activities, heartbeat payloads can carry forward across Activity Task retries so work can continue from its last checkpoint.
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LangGraph
In LangGraph, retry policies and error handlers are configurable per node, and behavior depends on the error type and policy. An interrupt is different: it pauses for human input and bypasses those retry and error-handling paths. Check the relevant framework documentation and configuration rather than assuming every error will retry automatically.
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Check workflow version and execution history
A recovery action can use a different workflow definition or execution data than you expect. Inspect both before triggering it, especially if the workflow has been edited since the failed run.
For n8n, the executions documentation describes retrying a failed workflow with previous execution data using either the currently saved workflow or the original workflow. After editing, the choice determines which definition is used. Availability can vary by deployment tier, so check the current interface and your deployment’s plan before relying on a particular control.
A safe recovery checklist
- Read the run status and stop reason. Determine whether the run awaits approval or input, was interrupted, or failed with an error.
- Inspect the error and execution history. Identify the failing step and whether it may already have changed an external system.
- Reconcile uncertain side effects. Check the destination system for a created record, sent message, or other action before repeating it.
- Confirm the recovery semantics. Check the platform’s checkpoint or continuation state, retry policy, replay boundary, and workflow version.
- Choose the least risky action. Keep holding for unresolved approval or uncertainty; resume an expected pause when ready; retry a known retryable failure only when duplicate effects are safe or reconciled.
Official product documentation establishes the behavior described for each platform, not a universal guarantee about every AI workflow or a comparative reliability ranking. Confirm the semantics for your framework version and deployment before taking an irreversible action.
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