Diagnose a missed agent deadline by tracing four separate events: whether the scheduler triggered, whether a worker began, whether the agent run finished, and whether the expected result was saved and verified. A run that started is not proof that the work completed. Compare records across those layers before retrying, because the cause may be a late trigger, queue delay, slow or failed agent step, or missing output.
Define what “missed” means
First record the scheduled time, the deadline, the specific result expected, and how you will verify that result. A missing notification, for example, could mean the agent never ran—or that it finished but failed to save or surface its output. Treat the following as hypotheses to distinguish, not diagnoses to assume:
- No scheduler trigger was emitted.
- A trigger was emitted but not enqueued.
- The job was queued, but no worker started it in time.
- The agent started but failed, stalled, or remained in progress.
- The agent completed after the deadline.
- The agent completed, but its result was not persisted or delivered.
Build one timeline across the system
Collect records from the scheduler, queue, worker, agent runtime, model and tool services, and output store. Put every timestamp in one explicit time zone. Record the intended schedule time, trigger or enqueue time, worker start, important step start and end times, run completion, and output persistence. Include the schedule or run ID, job or worker ID, trace ID, and external request IDs where available.
This timeline separates startup delay from execution time and post-run delivery delay. Compare the affected latency percentile with your own service or application baseline; one slow request does not establish a general pattern. OpenAI’s API troubleshooting guidance recommends supplying time ranges with time zones, request IDs where available, timestamps, error rates, and affected P50/P90/P95/P99 latency when investigating API errors or latency: Troubleshooting API Errors and Latency.
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Inspect the run and find the first delayed step
Open the run record or trace and inspect its status, recorded inputs and outputs, durations, errors, model generations, tool calls, handoffs, and guardrail events. Find the earliest step that was delayed or failed, then examine its dependency: for example, a rate limit, overloaded service, model-call timeout, slow tool, worker resource constraint, stalled approval, retry delay, or failed output write.
OpenAI’s Agents API documentation describes the tracing dashboard this way: “The tracing dashboard shows what your agent did, including each step’s recorded inputs, outputs, duration, and status.” Agents API tracing documentation. Traces are useful only if tracing is enabled and records are exported and retained. With the OpenAI Agents SDK, background export can delay when traces appear; call flush_traces() when you need traces delivered at the end of a unit of work. See the Agents SDK tracing guide.
A trace may not show scheduler dispatch, queue wait, or a downstream write. Correlate it with those systems instead of treating the trace as a complete record of the workflow.
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Separate timeout and retry limits
List every applicable limit rather than relying on one setting to explain the whole deadline:
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- Scheduler misfire or catch-up behavior.
- Queue visibility period or worker lease.
- Worker execution timeout and end-to-end run deadline.
- Individual model-call and tool timeouts.
- Retry count, backoff, and any upstream HTTP or proxy timeout.
In the OpenAI Agents SDK, the configured model timeout applies to an individual model-call attempt. It does not bound the entire agent run, function-tool execution, or retry backoff. Measure these durations separately and enforce an end-to-end deadline in the orchestration or application layer appropriate to your deployment. The SDK’s models documentation explains model configuration and timeouts.
Before retrying, check whether the run already created a session, completed some actions, or caused an external side effect. A retry may repeat work or hide a changing failure. For applicable errors, honor Retry-After, cap retries by attempts or elapsed time, and stop automatic retries if the error changes or a limit is reached. For tools that send or write externally, use idempotency keys or another deduplication strategy where available. OpenAI’s errors and recovery guidance covers outcome inspection and bounded recovery. SDK-managed timeout retries still need to follow replay-safety rules.
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Check persistence, alerting, and recovery
If the agent appears to have completed, verify the intended outcome in its destination: the database row, file, message, ticket, or other expected artifact. A successful run status alone does not prove that a downstream write succeeded or that a consumer could see the result.
For silent misses, monitor expected runs independently of the agent. An external scheduler or monitor can compare expected runs with completed outcomes and alert when a result is stale. Keeping this check outside the agent avoids relying on the same process or quota that may have failed.
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Use a repeatable incident checklist
- State the expected outcome: record the deadline, schedule, and verifiable artifact or action.
- Find the earliest missing event: check scheduler trigger, enqueue, worker start, run completion, and output persistence in order.
- Align timestamps and IDs: normalize time zones and correlate scheduler, job, trace, and request identifiers.
- Locate the first slow or failed step: inspect the trace and the corresponding queue, tool, service, or storage records.
- Check each timeout and retry scope: distinguish per-call limits from the whole-run deadline and any scheduler or worker limits.
- Assess side effects before recovery: inspect completed actions, use safe replay or deduplication, and bound retries.
- Verify the result and prevent recurrence: confirm persistence, then add independent stale-run alerts or assess durable execution if the failure pattern warrants it.
The precise behavior of schedule catch-up, time zones, concurrency, and misfires depends on the scheduler and deployment. The OpenAI documentation cited here explains agent tracing, timeout, recovery, and durable-execution capabilities; it does not determine how an unspecified scheduler or queue behaves. For workflow evaluation, see Evaluate agent workflows.
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