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A success status is trustworthy only when it follows evidence that the agent executed and the task’s acceptance criterion was met. In this incident, the platform, implementation, event records, and any remediation are unidentified, so the cause cannot be established. Treat it as an unresolved diagnostic problem: preserve the run record, trace the status transition, and compare the claimed result with independent evidence.
What a success status does—and does not—prove
An agent’s completion message is a claim. It does not, by itself, prove that the platform observed execution or that the requested outcome occurred. A verifier should connect an explicit acceptance criterion to evidence captured from the run or the system the task changed.
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AgentDock’s Verification documentation describes checking acceptance criteria against captured evidence—often command output—instead of trusting the task’s own completion report. That is a useful distinction: a status should reflect what the verification system established, not merely what the agent said.
How to investigate the mismatch
- Preserve the run record. Save the run identifier, claimed terminal state, timestamps, and relevant event or trace data before records expire or are overwritten.
- Find the success transition. Identify the event or condition that causes the platform to mark a run successful. Determine whether that transition can occur without an execution-start event or a tool result.
- Compare status with execution evidence. Look for a recorded tool invocation and its result, then compare their timing and source with the success event. A trace can help reconstruct activity, but only if it captures the relevant execution path.
- Check the outcome independently. If the task changed external state, read that state back from the system of record or use another independent ground-truth check. Tie the check directly to the task’s acceptance criterion.
- Investigate plausible event-handling issues. Check for retries, duplicate or delayed events, stale status, asynchronous workers, and uninstrumented ways an agent might run. These are hypotheses to test, not established causes of this incident.
- Reproduce before calling a fix effective. Test a run that never starts, or one with a deliberately absent execution event, and confirm it cannot silently become a pass. No correction or test result is established for this incident.
What traces can tell you—and what they cannot
OpenAI’s Agents API documentation describes session logs and event history for following agent activity. Its tracing documentation describes span-level inputs, outputs, tool calls, status, and duration. These records can help establish what the instrumented system observed and when.
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A missing or incomplete trace is not proof that execution did not happen. It may point to a capture or instrumentation gap. Confirm where each event came from and whether instrumentation covers every path by which the agent can run. A trace is useful only to the extent that the relevant events are actually captured.
Choose evidence that matches the task
| Verification approach | Evidence source | Independence from agent | What it establishes |
|---|---|---|---|
| Completion assertion | The agent’s own report | No | That the agent claimed completion; not that execution or the desired outcome was observed. |
| Captured execution evidence | Recorded tool invocation and output | Depends on the verifier and capture path | That an instrumented execution produced the recorded evidence; it does not necessarily prove an external change persisted. |
| External ground-truth check | Read-back from the target system or another independent check | Yes, when run by platform-controlled verification logic | Whether the relevant state or outcome matches the task’s criterion. |
Dreadnode’s verification documentation describes checking files, server-side state, or recorded trajectory. It also cautions that transcripts show what an agent said and tried, not necessarily what actually happened. For tasks that change external state, a read-back tied to the criterion is stronger evidence of the outcome than a transcript alone.
Design status handling so uncertainty is visible
Make verification platform-controlled and criterion-based. A missing, failed, or inconclusive check should not silently become a pass; retain a distinct unverified or error state where appropriate. The exact state design depends on the platform, which is not identified here.
Workflow evaluations can help surface recurring patterns across traces, but they do not identify the root cause of this particular mismatch. OpenAI’s agent evaluations documentation describes evaluation approaches for agent workflows; such evaluations complement incident-level event and outcome checks rather than replacing them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What remains unknown in this incident
The available account does not identify the platform, its implementation, the events recorded for the affected run, or a confirmed correction. There is therefore no basis to name a root cause, claim the defect was fixed, or say that a remediation was tested. Nor is there an established frequency for this failure mode: the cited product documentation is guidance, not a prevalence study.
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