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A success label does not prove an agent ran. It is trustworthy only when the platform ties that status to captured execution evidence and, where relevant, an independent check of the resulting system state. In the incident described here, the platform and its event records are unidentified, so the cause—and whether any correction worked—cannot be established. The right next step is to reconstruct the status transition from the run’s events, then test the claimed outcome against its acceptance criterion.
What a success status should prove
Keep three separate questions in view: Did the platform observe an execution start? Did that execution produce the expected evidence? Did the requested outcome actually occur? An agent’s completion message addresses none of these by itself; it is a claim, not proof.
A useful model is criterion-based verification: define the acceptance criterion, capture evidence produced by the execution, and have platform-controlled logic compare the evidence with the criterion. AgentDock’s Verification documentation describes this approach and says checks should use real captured evidence—often command output—rather than rely on an agent’s own report.
Reconstruct the run before guessing at the cause
- Preserve the record. Save the run identifier, the claimed terminal status, timestamps, and relevant event or trace data before logs expire or are overwritten.
- Find the success transition. Identify the exact event, callback, or state update that caused the platform to mark the run successful. Determine whether that transition can happen without a recorded execution start or tool result.
- Compare status with execution evidence. Look for a start event, tool invocation, and corresponding result. OpenAI’s Agents API documentation describes session logs and event history; its tracing documentation describes span-level inputs, outputs, tool calls, status, and duration. These records can help reconstruct activity, but only for paths the instrumentation actually captures.
- Check the target system independently. If the task was meant to change external state, read that state back from its system of record or use another independent check. Dreadnode’s verification documentation distinguishes a transcript of what an agent said or attempted from evidence of what actually happened, and describes checking files, server-side state, or recorded trajectory.
- Test plausible failure paths. Examine whether retries, duplicate or delayed events, stale status, asynchronous workers, or an uninstrumented execution path could explain the mismatch. These are hypotheses to investigate, not established causes of this incident.
A missing trace is not conclusive proof that execution never happened: the path may not have been instrumented, or event capture may be incomplete. Confirm where each event originated and whether every way of starting an agent passes through the same capture mechanism.
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Compare verification by the evidence it uses
| Evidence source | What it can establish | Main limitation |
|---|---|---|
| Agent completion assertion | What the agent reported | Does not establish that execution occurred or the outcome is real |
| Captured tool output or execution trace | Recorded activity, inputs, outputs, tool calls, status, and timing | Useful only if capture is complete across the relevant execution path |
| Independent read-back from the target system | Whether the desired external state is present | Must be tied to the task’s actual acceptance criterion |
For each check, ask whether its evidence source is independent of the agent, whether the trace covers all execution paths, and whether the check directly tests the acceptance criterion. A strong verification flow uses captured execution evidence and an independent outcome check when the task changes external state; a transcript or success message alone is weaker evidence.
Make unverified outcomes distinct from success
Verification should run under platform control against explicit criteria. A missing, failed, or inconclusive check should not silently become a pass. Preserve a distinct unverified or error state so operators can tell a confirmed success from a run whose evidence is absent or insufficient. The appropriate state names and transition rules depend on the platform’s design.
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Before calling a correction effective, reproduce the failure mode—for example, submit a run that never starts or deliberately omit the expected execution event—and confirm that the platform does not mark it successful. No reproduction or fix test is established for the incident described here.
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What remains unknown in this incident
The platform, implementation, event records, root cause, and remediation are not identified. There is also no incident-specific evidence that determines whether the agent failed to start, the trace was incomplete, or a status event was mishandled. Workflow evaluations can help surface recurring issues across traces; OpenAI describes this in its agent evaluations documentation. Such evaluations may help find patterns, but they cannot identify the cause of this particular incident without its records.
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No frequency estimate is established for platforms recording success when agents never ran. The available product documentation is not a prevalence study, so a rate would be speculation.
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