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First, identify what kind of pause occurred
An approval interruption is an expected workflow state: the agent is waiting for a decision before it can proceed. It is different from a tool failure, guardrail failure, validation error, or run-limit error. Handle those as errors or limits, not as requests for human approval. OpenAI distinguishes expected approval interruptions from runtime and validation failures in its guardrails and human review guidance.
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The pause signal and continuation interface depend on the runtime. The OpenAI Agents SDK exposes interruptions and resumable run state; LangGraph has its own interrupt-and-resume workflow. Treat them as separate APIs, not interchangeable names for the same mechanism.
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For an OpenAI Agents SDK workflow, retain the pending interruption and its associated RunState. When a review is delayed, the SDK supports serializing that state for later continuation. Once the human decision is available, resolve the interruption and resume from the saved state rather than submitting the original request as a fresh turn. See the SDK’s human-in-the-loop documentation and OpenAI’s running agents guide.
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This preserves the context and pending operation that produced the pause. Starting over can lose that relationship and may cause the agent to generate a different operation instead of continuing the one the reviewer saw.
Keep the gated action behind the review boundary
For a configured approval-gated tool, the documented lifecycle records an approval interruption instead of executing the tool. The application receives the pending item and resumable state; after approval or rejection, it continues the run with the decision. Do not execute the action while waiting for a reviewer. OpenAI describes this lifecycle in its approval documentation.
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Show the reviewer the exact pending action and enough context to make a decision. The API documentation establishes the approval and rejection flow, but does not prescribe a universal screen layout. Your application must also ensure the reviewer is authorized to decide on that action.
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Use the continuation mechanism for your framework
OpenAI Agents SDK
Resolve the interruption on the saved run state and resume that state. The approval applies to a specific pending tool call; it is not equivalent to asking the agent the same question again.
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LangGraph documents a different pattern: interrupt the workflow for human input, then resume with a Command containing the human response. Follow the framework’s own human-in-the-loop instructions rather than adapting the OpenAI SDK’s state API.
Account for streaming and session-backed history
Streaming runs
Do not inspect a stream for pending interruptions before it has settled. The JavaScript SDK guidance is to wait for the stream to complete, inspect interruptions, resolve them, and resume from the stream’s state. Its human-in-the-loop guide documents the interruption flow, and its results guide covers resumable state and continuation context.
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Session-backed conversation history
If your application uses SDK sessions to store conversation history, resume with the same session, or with a session configured for the same ID and backing store. That lets the continued turn append to the stored conversation instead of creating a separate history. See the JavaScript SDK’s sessions guide.
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- Classify the result. Determine whether the runtime reported an expected approval or human-input interruption, rather than a failure or limit.
- Retain what continuation requires. Store the pending interruption and the runtime’s resumable state. For OpenAI SDK flows, the approval is attached to a
RunState, which can be serialized when review is delayed. - Present the decision clearly. Give the reviewer the exact pending operation and the context needed to approve or reject it.
- Apply the response to the interruption. Resolve the pending item, then resume the existing run using the framework’s continuation interface.
- For streamed runs, wait first. Let the stream settle, inspect its interruptions, and then resume from its saved state.
- For session-backed history, preserve session identity. Continue with the same session or a session configured for the same ID and backing store.
Decide the production details your runtime does not settle
Pause and resume documentation does not establish a universal policy for approval expiration, reviewer identity and authorization, durable storage guarantees, retries, idempotency, or a crash during resumption. These choices depend on the runtime and on the side effects your application permits. Define them explicitly before relying on delayed review in production. OpenAI’s approval guidance advises evaluating sensitive actions and enforcing review and execution boundaries in the application, but it does not settle every storage or retry design.
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When choosing a workflow pattern, consider who owns the state, what kind of pause the runtime represents, how a stream signals completion, how conversation history is continued, and whether the action is held until the right reviewer decides on the exact operation. OpenAI’s running agents guide describes conversation continuation strategies, while its results guide discusses resumable state and context.
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