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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →If nobody answers an agent’s approval request before its deadline, the agent should follow an explicit timeout policy: escalate to another authorized reviewer when appropriate, or block the pending action and report that it timed out. Silence should not count as approval unless the application’s risk policy explicitly says so.
What should happen when an approval times out?
Treat the deadline as a workflow decision point, not as permission to continue. For consequential external actions, the safer default is to stop the gated operation or send it to an authorized alternate reviewer. AWS recommends defining timeout policies and escalation paths, with a safe fallback that typically blocks the operation: AWS Well-Architected Agentic AI Lens, AGENTSEC04-BP02.
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Make the outcome visible to the requester. Distinguish a timed-out request from an approval or rejection, and explain whether it was stopped, escalated, or can be resubmitted. If the system supports it, preserve the pending workflow so an authorized person can still resolve it; do not let a late response silently trigger an action after the request has expired.
How should risk determine the fallback?
Choose the timeout outcome according to the impact and reversibility of the protected action. A reversible, low-impact task may be eligible for a carefully defined application policy, while actions with significant external consequences should remain blocked until an authorized decision arrives. Do not infer approval from an empty response or an expired timer.
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- High-impact or hard-to-reverse action: block the action on timeout, or escalate to an authorized reviewer before proceeding.
- Lower-risk action: use an alternative outcome only if the application’s risk policy expressly permits it and the resulting behavior is clear to users.
- Any action: make the timeout outcome observable and record it; avoid an indefinite wait with no status or recovery path.
How to design the approval timeout
1. Put the gate in the layer that can enforce the policy
Choose an approval mechanism whose owner can reliably pause, time out, and resume or close the workflow. The implementation patterns differ: the OpenAI Agents SDK can interrupt a tool call and resume a serialized run; Cloudflare Agents documents durable workflow approval; and AWS describes callback waits in Step Functions, application-managed confirmation or return of control for Bedrock Agents, and asynchronous AgentCore tasks. The application or workflow layer must still enforce the chosen timeout policy.
2. Set a bounded approval deadline
Choose a wait duration that fits reviewer availability, urgency, and action risk, then configure it in the workflow or application layer. A model call’s network timeout is not necessarily the deadline for human approval. The cited framework documentation does not establish a universal approval duration or default.
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3. Define explicit workflow states and transitions
At minimum, distinguish pending, approved, rejected, timed out, and escalated. Configure the timeout transition rather than relying on an implicit default. For example, AWS describes Step Functions TimeoutSeconds or HeartbeatSeconds with Catch routing; Cloudflare’s example handles missing approval data by escalating or auto-rejecting. In Cloudflare’s example, the workflow may continue without approval data when the timeout expires, so application code must check for that missing data rather than treat continuation as approval.
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Do not run the operation that requires approval until the approval decision has been resolved. Make retry and resume behavior safe for the specific operation: the framework sources describe pause and resume patterns, but do not prescribe one universal idempotency implementation.
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5. Persist the request and record its outcome
Store enough durable state to resume or close the request cleanly. OpenAI’s SDK describes converting a paused result into RunState, resolving an interruption, and resuming the original run. The Microsoft Agent Framework says pending requests are saved in checkpoints and re-emitted when restored.
Keep an audit record of the operation, request and expiry times, reviewer identity, response time, decision, escalation events, and final outcome. AWS specifically recommends recording reviewer identity, timestamps, operation, decision, and escalations, and making the operation, relevant data, and consequences available in the approval interface.
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How implementation patterns differ
| Pattern | Useful when | Who handles timeout and resumption? |
|---|---|---|
| SDK interruption and serialized run state (OpenAI Agents SDK) | A tool call pauses an agent run and the application can resume it later. | The application stores and resolves the paused state, and decides when and how to resume. |
| Durable workflow approval (Cloudflare Workflows) | A task may need to wait beyond an active agent or server request. | The workflow provides a durable wait; application code handles the timeout result. |
| Workflow callback wait (AWS Step Functions) | Agent work runs in a longer-lived state machine, often with separate reviewer roles. | State-machine timeout and catch or escalation transitions govern the wait. |
| Checkpointed request and response (Microsoft Agent Framework) | A workflow pauses for human input and may need restoration from a checkpoint. | The checkpoint preserves pending requests; the application handles the response and policy. |
| Approval-aware task (Apache Airflow common AI provider 0.9.0 documentation) | Human review is part of an Airflow task. | The provider exposes approval_timeout; timeout behavior is version-specific and may surface as a timeout error. |
These are implementation patterns, not interchangeable defaults. Check the documentation for the framework version you deploy and make the application-level outcome explicit.
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