Trace the job through four separate events: the scheduler fired, a worker started, the agent run finished, and the intended result was saved and verified. A missing or late result can originate at any boundary, so a run-start log alone does not prove the work completed. The steps below help locate the delay or failure without assuming a particular scheduler, queue, cloud, or agent framework.
Define what “missed” means
Write down the scheduled time, the exact result expected, and the evidence that counts as success—for example, a record in a destination system or a completed status visible to the requester. Then classify what you observed:
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- No scheduler trigger is recorded.
- A trigger exists, but no job was enqueued.
- The job was queued, but no worker began it.
- The agent started but failed, stalled, or remained in progress past the deadline.
- The agent completed, but its output was not persisted or surfaced.
These are investigation branches, not diagnoses. Scheduler behavior such as time-zone handling, missed-run catch-up, and concurrency varies by deployment; check the configured scheduler’s own records and settings.
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Collect records from the scheduler, queue, worker, agent runtime, model and tool services, and output store. Put every timestamp in one explicit time zone, and include the intended schedule time, actual trigger or enqueue time, worker start, important step start and end times, run completion, and output persistence or verification.
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Correlate records using whatever identifiers your deployment exposes: schedule or run ID, job or worker ID, agent trace ID, and external request IDs. OpenAI’s API troubleshooting guidance asks for time ranges with time zones, request identifiers where available, timestamps, error rates, and affected P50/P90/P95/P99 latency (OpenAI API troubleshooting). Compare the affected percentile with your service or application baseline; one slow request does not establish a general pattern.
Read the gaps, not just the total duration
- A late trigger points toward scheduling or dispatch behavior.
- A prompt trigger followed by a long enqueue-to-worker gap points toward queue wait or worker availability.
- A prompt worker start with a long agent run points toward model, tool, approval, retry, or resource delays.
- A recorded completion followed by a missing result points toward output persistence, downstream delivery, or verification.
These comparisons narrow where to look; confirm the explanation in the system responsible for that interval.
Inspect the run and its slowest step
Open the available run record or trace and inspect status, recorded inputs and outputs, durations, errors, model generations, tool calls, handoffs, and guardrail events. The OpenAI Agents API documentation says, “The tracing dashboard shows what your agent did, including each step’s recorded inputs, outputs, duration, and status” (OpenAI Agents API tracing).
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Find the first step that was delayed or failed, then follow the dependency it relied on. Possible explanations include rate limiting or overload, a model-call timeout, a slow tool service, worker resource constraints, a stalled approval, retry backoff, or a failed write to the output store. A trace may not show scheduler dispatch, queue wait, or a downstream write, so join it to those systems’ records rather than treating the trace as the whole story.
Tracing must be enabled, exported, and retained to be useful. The Agents SDK tracing documentation notes that export can happen in the background, so a trace may not appear immediately; flush_traces() is available when immediate delivery at the end of a unit of work is needed (Agents SDK tracing).
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Separate each timeout and deadline
Record the limits that apply at each layer instead of relying on a single setting:
- Scheduler misfire or catch-up behavior.
- Queue visibility or lease duration.
- Worker execution timeout.
- Whole-run or end-to-end deadline.
- Individual model-call timeout.
- Tool timeout and upstream HTTP or proxy timeout.
- Retry count, backoff, and maximum elapsed time.
In the OpenAI Agents SDK, the configured model timeout bounds an individual model-call attempt; it does not bound the entire agent run, function-tool execution, or retry backoff. See the SDK’s model configuration documentation. If the user-facing promise is “finish by this time,” enforce that end-to-end deadline in the orchestration or application layer appropriate to your deployment, and measure the constituent waits and calls separately.
Retry without creating duplicate work
Before retrying, inspect the current run or session and its completed actions. An apparent failure may have happened after a side effect succeeded, such as sending a message or creating a record. For externally visible writes, use idempotency keys or another deduplication strategy where available.
OpenAI’s errors and recovery guidance recommends checking outcomes and completed actions, honoring Retry-After when applicable, setting an attempt limit or deadline, and stopping automatic retries when the error changes or a limit is reached (Errors and recovery). Apply the same caution to SDK-managed retries: a timeout’s retry classification does not make replaying side effects safe. Bound both attempts and total elapsed time so retries cannot silently consume the deadline.
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Trigger or dispatch never happened
Check the scheduler’s run history, time-zone configuration, misfire and catch-up policy, and dispatch destination. If the scheduler records a trigger but the queue does not, investigate the handoff between those systems.
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Queued work did not start promptly
Inspect queue age, worker health and capacity, concurrency limits, and lease or visibility behavior. If a worker claimed the job but never recorded a start or completion, check worker logs and restart recovery rather than rerunning blindly.
The agent run consumed the available time
Use the trace to identify expensive or failing steps; inspect model and tool service errors, resource constraints, approvals, and retry delays. If the deadline is for the whole task, adjust the orchestration-level budget rather than assuming a per-model-call timeout governs all work.
Completion was not durable or visible
Verify the output store and downstream delivery path independently of the agent’s completion status. Define success using a persisted, queryable outcome when possible, and alert on the absence of that outcome rather than only on agent-process errors.
Decide whether durability or independent monitoring is needed
If long waits or process restarts lose progress, evaluate durable workflow orchestration against your actual failure modes and test what happens after a restart. OpenAI Agents SDK documentation names integrations for Dapr, Temporal, and Restate for durable or long-running workflows; this is a list of capabilities, not a comparative ranking or a recommendation for every deployment (Agents SDK running agents).
If the operational problem is silent failure, put monitoring outside the agent being monitored. An independent monitor can compare expected runs with completed, verified outcomes and alert when a result is stale, without relying on the same agent process or quota. The monitoring design should account for the scheduler and output system you actually use.
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Detailed inputs and outputs can help diagnose slow or failed steps, but they may contain sensitive content. Set access controls and retention deliberately, and record enough identifiers and timing data to join events across systems without making traces broadly accessible. For workflow evaluation beyond a single incident, OpenAI’s agent evaluation guide describes ways to evaluate agent workflows (Evaluate agent workflows).
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