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To make an AI agent’s work durable, decide where its state lives, represent approval as a resumable pause, validate at the boundary of each action, and make persistence safe to retry. These mechanisms preserve workflow progress, but they do not guarantee that an external action happened exactly once or that every failure can be recovered automatically.
What must survive when an agent stops?
Start by listing the state your workflow needs to recover. An agent may need its conversation history, current workflow progress, a pending human decision, and the status of an external action it attempted. These are related, but they are not interchangeable: preserving chat history alone does not establish whether a payment, message, or other side effect succeeded.
Then choose and document the owner of continuation state. OpenAI’s Running agents guide describes application-managed history, persistent sessions, and server-managed continuation identifiers as distinct approaches. Choose one strategy for a conversation unless your application deliberately reconciles multiple layers. Combining local replay with server-managed state without reconciliation can duplicate context.
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Choose a state strategy
- Application-managed history: Your application controls the conversation history and how it is replayed or stored.
- Persistent sessions: A session stores ongoing chat state and can support resumable work under application control.
- Server-managed continuation: A continuation identifier lets the runtime continue from server-managed state.
The appropriate choice depends on where you need control and how the surrounding workflow resumes. The documentation describes these mechanisms; it does not establish a universally best strategy.
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How do you resume an agent run after human approval?
Model approval as an explicit pause in the workflow, not as a new conversation that merely restates the request. In the documented OpenAI approval lifecycle, a sensitive action interrupts the run and produces resumable state. Your application obtains a human decision, then resumes the same run from that state. If review may be delayed, the state can be serialized and stored until a decision is available.
- Reach the governed action: Configure the workflow so that an operation requiring review interrupts before the sensitive side effect.
- Save the interruption state: Retain the resumable state associated with that run, including it in durable storage if approval is not immediate.
- Record the decision: Capture whether the reviewer approved or rejected the proposed action.
- Resume the same run: Supply the decision and continue from the saved state rather than starting over from an uncoordinated replay.
This lifecycle preserves run continuity. It does not, by itself, resolve an ambiguous timeout or partial completion in the external business system. The workflow still needs a way to determine whether an attempted side effect occurred before retrying it. OpenAI’s Guardrails and human review guide distinguishes human approval decisions from automatic checks.
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Where should validation and review happen?
Place each check at the boundary it is meant to govern. OpenAI’s guardrails guide identifies different boundaries for incoming requests, final responses, tool calls, and sensitive side effects.
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|---|---|---|
| Input guardrail | Incoming request | Check the input before the agent acts on it. |
| Tool guardrail | Function-tool arguments or results | Check the specific tool interaction and its data. |
| Output guardrail | Final response | Check the response the agent is about to return. |
| Human review | Before a sensitive side effect | Obtain an approval decision before the governed action executes. |
Do not assume an agent-level guardrail surrounds every tool call. The guide cautions that this may not be true in manager-style workflows. If a particular operation must be checked, put the control around that operation rather than relying on a broader check whose execution boundary is unclear.
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How can custom session history handle retries?
If you implement a custom session backend, use the Agents SDK’s transaction-aware persistence pattern. Its Sessions guide describes persisting a stable operation identifier and the associated history mutation in the same transaction. When that operation is retried, the backend can reconcile the same identifier without applying the history update twice.
- Assign a stable identifier to the history operation.
- Write the identifier and corresponding history change atomically in one storage transaction.
- On retry, use the same identifier so the backend can recognize and safely reconcile the operation.
This is a persistence technique for session history. It is not a blanket exactly-once guarantee for model calls, third-party tools, or business-system side effects. Those systems need their own handling for retries and uncertain outcomes.
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How should you evaluate an orchestration integration?
Compare candidate approaches against the actual failure modes and controls your workload needs. Temporal’s Durable AI documentation lists integrations for multiple agent frameworks, including OpenAI Agents SDK and LangGraph. That list establishes documented integration availability, not comparative superiority.
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- State ownership: Identify whether session history and continuation state are controlled by your application or managed by a runtime.
- Pause and resume: Verify how a pending approval is represented and how the original run continues after a decision.
- Action boundaries: Check where input, output, tool, and human-review controls actually execute.
- Persistence semantics: For a custom history backend, determine whether operation identifiers and history writes can be committed atomically.
- Framework support: Confirm that the orchestration layer documents support for the agent framework you use.
Cost, scaling, operational burden, and recovery performance require workload-specific evaluation. The cited documentation does not provide comparable measurements or benchmarks for those questions.
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