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Background AI processes often fail in production because the demo keeps a request, worker, and in-memory state alive, while production adds timeouts, restarts, retries, external-service delays, and approval waits. Use provider background mode to let a single long model response run without holding a client connection open; use persisted application state or durable orchestration when the whole workflow must survive interruptions.
Why does a background AI job that worked in the demo time out or lose progress?
A long model call can outlast the client connection or the worker handling it. Even if the model call finishes, the application may have to make more calls, update a database, wait for an outside service, or pause for a person. A demo that holds all of this in one process can hide the difference between waiting and recovering.
There are two separate problems to solve:
- Waiting: How can a caller learn when one slow operation finishes without keeping its original request open?
- Recovery: If a worker stops or a person takes hours to approve an action, how can the application resume the larger workflow with the right state and without duplicating effects?
An asynchronous API can solve the first problem. It does not, by itself, make every step in a multi-step agent workflow durable.
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Is the delay one model response or a whole workflow?
One long model response: poll a background response
OpenAI’s Responses API background mode starts a response asynchronously. The application can poll while it is queued or in progress, then inspect the terminal status and consume the output. This avoids making the client wait on one open connection for the entire model call; it does not preserve application-side progress or external side effects automatically. See the background mode documentation for the current API behavior.
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Account for data handling as well as latency: OpenAI documents that response data is temporarily stored to support polling, and that storage behavior is affected by storage settings and project data controls. Check the current documentation and your project’s controls against your retention requirements before enabling this mode.
Several steps, external work, or human approval: persist the workflow
If a job calls tools, changes records, waits for a third party, or needs a human decision, persist enough state to resume the workflow independently of the process that started it. A response ID can help retrieve one response; it is not a complete record of which business steps have happened, what is still pending, or whether an external action has already taken effect.
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Where should continuation state live?
Choose a state owner based on who needs to control, share, and recover the conversation or job. The OpenAI Agents SDK describes application-held history, sessions backed by storage, and server-managed conversation IDs. It recommends sessions for persistent memory, resumable approvals, or application-controlled storage; its agent runtime and continuation guide explains the options.
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- Persisted SDK session: Use a session backed by storage when turns need persistent memory or an approval flow must resume. Confirm the session’s backing store and sharing behavior match your deployment; an in-memory session in a demo is not durable persistence.
- Server-managed conversation ID: Keep a conversation identifier and continue against server-managed state when that ownership model fits. Decide how the application will associate the identifier with its own user, job, and authorization records.
- Workflow-engine history: Use a durable orchestrator when the application must resume a sequence of steps after waits, retries, or worker restarts. The workflow history tracks execution progress; application logic still determines what each step means and whether its external effects are valid.
For any option, persist the identifiers and business context needed to find the job after a restart. Define who can resume it and how an approval or cancellation changes its state; do not rely on a worker’s memory as the only copy.
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Which recovery layer fits the job?
| Approach | Fits when | What it handles | What remains your responsibility |
|---|---|---|---|
| Provider background mode plus polling | One model response may exceed the caller’s connection or request window. | Asynchronous response execution and status polling. | Persisting the broader job, handling terminal outcomes, and coordinating later steps or side effects. |
| Application-managed state or a persisted session | An agent needs to continue across turns or resume with application-controlled memory. | Conversation or session state, depending on the storage and continuation design. | Worker recovery, step scheduling, external-action deduplication, and long waits unless separately implemented. |
| Durable orchestration | A multi-step workflow needs to survive long waits, retries, process restarts, or human approval. | Durable execution and recovery according to the orchestrator’s programming model. | Correct business logic, safe external effects, and operational configuration. |
The OpenAI Agents SDK lists integrations for Dapr, Temporal, Restate, and DBOS for durable orchestration. Its documentation describes them as intended for runs that may span “long waits, retries, or process restarts.” Temporal’s documented OpenAI integration executes model calls as Activities so they can retry durably and are not repeated during workflow replay. See the Agents SDK integrations and the Temporal OpenAI integration. These are different capabilities, not evidence that one orchestration choice is best for every application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a long-running agent handle retries and side effects?
Treat model calls, tool calls, database writes, and outside API actions as distinct steps with explicit failure behavior. A retry can run work again; even when an orchestrator provides durable scheduling, that does not make an irreversible external effect safe to repeat.
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- Set a timeout for each step. Choose it for that dependency and operation rather than letting a whole job hang without a bound.
- Classify failures. Separate transient infrastructure problems from failures that require corrected input, a business decision, or human intervention. A policy-blocked action should not be automatically redispatched.
- Bound retries. Set a maximum number of attempts and backoff for retryable errors. OpenAI’s deployment checklist advises checking status and error codes, honoring
Retry-Afterwhen supplied, and otherwise using bounded exponential delays with jitter for appropriate transient errors. - Make external effects safe to repeat. Use idempotency keys or deduplication where the external system supports them; otherwise, design reconciliation so the workflow can determine whether an action already happened before trying again.
- Checkpoint at meaningful boundaries. Record completion and the data required to continue. Temporal’s task documentation distinguishes task failures from workflow execution failures and describes activity heartbeat payloads for carrying checkpoint information across retries.
- Handle terminal states explicitly. Record whether the job completed, failed, was cancelled, or is waiting for a person. Do not treat a successful poll or a retryable intermediate status as proof that the business task is complete.
How do you decide whether to add durable orchestration?
Start with the real wait and recovery requirements rather than the label “background agent.” A short, isolated model response usually does not need a workflow engine. The case for one grows when a job includes several independently failing steps, must remain pending while people or services respond, or needs to continue after workers are replaced.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- How long can the job wait, and does it need to wait for a human approval?
- Must it resume after a process restart, deployment, or worker loss?
- Which system owns conversation state and execution history, and can another worker access them?
- Which steps may be repeated, and which require deduplication or reconciliation?
- Can the team operate and observe the added orchestration layer, and does it fit the existing stack and cost constraints?
Durable execution addresses recovery of workflow progress; it cannot guarantee that a business action is correct, authorized, or successful. Keep validation, authorization, and external-system error handling in the application design.
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