Verify side effects before retrying agent tasks after an OpenAI outage

OpenAI's [October 13 incident](https://status.openai.com/incidents/jwna590w) degraded GPT‑6.1 Sol across ChatGPT Work and four Codex surfaces. OpenAI reported mitigation and recovery, but did not describe how in‑flight tasks behaved. For a stateful agent task, an error can appear before an external action starts, while it runs or after the destination accepts it. The same visible timeout can therefore represent no change, a complete change or a partial chain. This article describes a recovery control, not a claim that OpenAI duplicated actions. The public incident record does not report duplicate deployments, pull requests, messages or writes.

List what the task was allowed to change: local files, Git branches and commits, pull requests, deployments, tickets, messages, scheduled jobs and application records. Check each destination directly using a read‑only query. For file work, inspect status and diff. For Git hosting, search the intended branch, commit and pull‑request title. For deployment systems, read the build history and active version. For APIs, query by the client‑generated operation key or business identifier. Preserve timestamps and IDs from both the agent task and the destination. A matching remote object created during the incident window is stronger evidence than the missing final chat response.

A retry is safest when the destination can recognize the same logical operation. Reuse the original idempotency key for an uncertain API mutation, and preserve the exact request body if the contract requires byte‑for‑byte identity. For versioned records, re‑read the current revision and honor `If‑Match` or another compare‑and‑set precondition. For Git, continue from the existing branch and commit instead of generating another branch with equivalent changes. When a service lacks idempotency, add an application‑level duplicate check before mutation. A human‑readable title alone may not be unique enough; prefer a stable task or operation identifier.

OpenAI's [ChatGPT troubleshooting guide](https://help.openai.com/en/articles/7996703‑troubleshooting‑chatgpt‑error‑messages) suggests regenerating a failed response after transient errors. That is appropriate when the output is only text and has no side effects. An agent can also run commands and use connected systems. In those cases, first ask whether the original action may already exist. A fresh task can make reconciliation harder: OpenAI's [Codex Cloud guide](https://help.openai.com/en/articles/20001545‑using‑codex‑cloud) says a new task has a separate workspace and does not recover another task's uncommitted changes. The safe sequence is read, reconcile, then retry. Do not turn a provider recovery into a second operational incident.

For important agent work, retain the model, surface, task ID, start time, last confirmed step, intended side effects and stable operation keys. Close with a direct acceptance check against the destination. If the task stops, that receipt identifies the exact uncertainty boundary. The operator can verify a few named states instead of rerunning the entire workflow. It also gives Support a precise timestamp and surface if the problem persists after the shared incident closes. OpenAI resolved the provider incident at 16:36 UTC. Local completion and external side effects still need their own evidence. Follow [OpenAI's OpenTools organization page](https://opentools.ai/organizations/openai) for related reliability coverage. *Figure: The safe retry boundary for a stateful AI‑agent task: observe, reconcile, reuse controls and verify. Sources: [OpenAI Status](https://status.openai.com/incidents/jwna590w), [Using Codex Cloud](https://help.openai.com/en/articles/20001545‑using‑codex‑cloud) and OpenAI's [ChatGPT troubleshooting guide](https://help.openai.com/en/articles/7996703‑troubleshooting‑chatgpt‑error‑messages). Figure by OpenTools Team; no third‑party expressive material used.*