A retry repeats an operation; it does not undo the first attempt. If an agent timed out after sending an email, charging a card, writing to a database, or deploying code, a second attempt can repeat the action rather than erase it. Safe recovery depends on which system owns the state, what the first attempt actually accomplished, and whether repeating its side effects is safe.
Retry, replay, rewind, and resume are different operations
Agent systems use several recovery mechanisms that can sound alike in a UI or log. They operate on different things, so check what each mechanism changes before relying on it.
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| Operation | What it changes | Key safety question |
|---|---|---|
| Retry | Repeats a request or operation under a policy. | Could the earlier attempt already have taken effect? |
| Replay | Sends prior input or history again. | Which state owner receives it, and could provider or tool work repeat? |
| Session rewind | Removes persisted history items attributed to an attempt. | Can the runtime identify and remove exactly the failed attempt’s items? |
| Checkpoint resume | Continues a workflow from saved state or a failure boundary. | Are earlier steps committed, and can any repeated side effects be made safe? |
| Compensating action | Performs a new operation intended to counteract an earlier effect. | Is a correct compensation possible for this specific side effect? |
A compensating action is not a rewind: it creates another event, and may not fully reverse the original. For example, a refund is a separate transaction, not the erasure of a charge.
Why a failed attempt can still have succeeded
A timeout or dropped connection can tell you that the caller did not receive a usable result; it does not necessarily tell you whether the provider or external service received the request. The service may have completed the work before the response was lost. Retrying without resolving that uncertainty can create a duplicate.
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The OpenAI Agents SDK documents an explicit approval for replaying requests marked unsafe, because provider-side work might already have happened. Its model-retry documentation also describes cases that remain blocked: streamed output once it has started, requests vetoed because replay could cause local side effects, and stateful follow-up requests whose replay safety is unknown. These are behaviors of that SDK, not universal rules for every agent runtime. See OpenAI Agents SDK Models.
The SDK’s results guide makes another important distinction: it can preserve one durable input occurrence within its own run state, but that does not guarantee exactly-once delivery to the model provider. If an unsafe replay is approved after a request may have reached the provider, provider-side work may happen again. See OpenAI Agents SDK Results.
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Identify the state owner before continuing
“Continue the conversation” can mean replaying local history, reusing a client-managed session, or continuing server-managed state. Those choices are not interchangeable. If one layer resends history while another already retains the conversation, the agent can receive duplicated context.
OpenAI’s guide to running agents distinguishes application-managed result history, client-managed sessions, server-managed Conversations API state, and Responses API continuation using a previous response ID. It recommends choosing one continuation strategy per conversation in most applications. It also distinguishes an expected approval pause—which should resume from the same state—from starting a new turn. These are OpenAI-specific examples; apply the equivalent state-ownership rules for whichever runtime you use. See Running agents.
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- Client-managed session: The session store owns continuation. Inspect how a retry interacts with its persisted items.
- Server-managed conversation or response state: Continue using the service’s supported state reference rather than blindly replaying a full local transcript.
Rewind only the history you can prove belongs to the attempt
Removing conversation items can clean up persisted session history; it cannot reverse an independent external action. A session rewind should be narrow: identify the exact serialized suffix written by the failed attempt, verify the full suffix before removing anything, and avoid deleting earlier or concurrent work.
The OpenAI Agents SDK session-persistence guidance describes retry cleanup as best effort. It calls for restoring any items already popped if a pop fails or returns unexpected data, and for waiting for asynchronous cleanup before retrying when stale tail items might otherwise be read. This is implementation guidance for that SDK’s session-history cleanup, not a general rollback API. See Session Persistence.
Make repeated side effects safe
Checkpointing saves progress; it does not make an operation safe to repeat. AWS’s Well-Architected Agentic AI Lens puts it plainly: “Checkpointing is only useful if recovery is safe, and recovery is only safe if steps are idempotent.” Idempotency means that repeating an operation does not create an additional unintended effect. See AWS checkpoint-based recovery guidance.
- External calls: Use a stable idempotency key when the service supports one, so repeated requests for the same logical operation can be recognized.
- State mutations: Use conditional writes or an equivalent concurrency guard to prevent stale or duplicate updates.
- Events: Deduplicate emissions or consumption where the event system supports it.
- Workflow progress: Record meaningful boundaries and enough evidence to distinguish attempted, accepted, completed, and verified work.
AWS describes Amazon Bedrock AgentCore Runtime as supporting persisted filesystem state across stop and resume for long-running workloads, and AWS Step Functions as supporting workflow-stage-aware checkpointing and restart from a failure point. Those are vendor-described options, not guarantees that any particular workflow is safe to replay. The safety still depends on the steps and side effects in that workflow.
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Use this decision sequence after a failure
- Classify the failure. Determine whether the operation was rejected before execution, failed during execution, or may have completed while its response was lost.
- Check the execution record and state owner. Inspect provider, tool, application, session, and workflow records as applicable. Establish what is known about acceptance, completion, and verification.
- Choose the smallest recovery scope. Retry a request, rewind a verified session suffix, or resume a workflow checkpoint only if that mechanism matches the state that needs repair.
- Check replay safety. Use an idempotency key, conditional write, or deduplication where supported. If success is ambiguous and no safe deduplication mechanism exists, do not assume an automatic retry is harmless.
- Authorize or block replay deliberately. If a runtime warns that work may repeat, treat that as a consequential decision—not as a routine way to clear an error.
- Verify the outcome. Confirm the external effect and the persisted agent state separately; success in one does not prove success in the other.
A workspace restore is not a global rollback
Visual Studio Code’s agent recovery guidance is a concrete example of a limited restore boundary: restoring a checkpoint can address workspace or chat state, but it does not reverse terminal commands, network requests, deployments, or changes to external services. A user-facing “restore” control should therefore be described by the state it actually restores, not as rewinding the whole agent run. See Get an agent back on track.
What to document in an agent workflow
Make the recovery boundary observable to both operators and users. At minimum, record the operation’s identity, its state owner, the relevant checkpoint or session boundary, and whether external work was attempted, accepted, completed, or verified. For every retry path, specify what happens when delivery status is ambiguous and whether replay requires explicit approval.
Keep the language precise: “restores this session suffix,” “resumes from the workflow checkpoint,” or “retries the request after replay safety is confirmed” is more informative than “rolls back the agent.” A checkpoint can restore only the state within its boundary; external effects need their own deduplication, verification, or compensation strategy.
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