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Start by identifying whether the failure happened when the request was submitted, after a turn or session began, or inside the execution environment. “Agent preflight validation failure” is a useful umbrella description, not a universal error category: the checks and error fields depend on the platform. The examples below apply to the named OpenAI APIs, SDK, and ChatGPT Workspace Agents guidance—not to every agent product.
First, identify which layer failed
Separate a rejected API request from a failure during an already-started turn or session, and from an environment or tool failure. These cases have different evidence and remedies.
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- Request rejected: Check the HTTP status and response
errorobject. - Turn or session failed: Retrieve that turn or session and inspect its status and error.
- Environment failed: Inspect the reported environment error.
OpenAI’s Errors and recovery guidance recommends using error.code in application logic and error.message to explain the failure. For request-validation errors, error.param may identify the field to correct, but not every response includes a parameter or a code your application already recognizes.
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Work through API request and configuration problems
Use the error code as a clue, then verify the request against the documentation for the API version you are calling. The official error guide lists several distinct cases; their names and applicability should be checked against the current guide rather than assumed to apply across products.
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| Error example | What to inspect |
|---|---|
invalid_request_error |
Input or configuration that is invalid or too large; use the reported parameter, when present, to locate the field. |
invalid_beta |
The beta header; confirm it is valid for the API version and endpoint in use. |
invalid_otlp_endpoint or invalid_otlp_header |
Tracing endpoint or header configuration. |
agent_not_persisted |
The agent ID. This indicates a session-local agent was supplied where a saved agent is expected; create a saved agent and use its ID. |
unauthorized or forbidden |
Whether credentials authenticate the caller and whether that caller has the required access. |
| Not-found or model-not-found errors | Whether the requested resource or model exists and is available to the caller. |
One additional boundary applies when using the OpenAI Agents SDK: its guide says Standard Schema tool parameters are converted to JSON Schema for the model and validated locally before the tool’s execute function runs. If the top-level request was accepted but a tool never executes, inspect the tool arguments and local validation path as well as the API response. This behavior is specific to the cited SDK guide.
Check agent access and Workspace setup
For ChatGPT Workspace Agents, verify the person’s access to the agent and the permission needed for the intended action—accessing, editing, or publishing. Also check whether the required app is enabled and its account connection is in place. If a schedule or API channel is not live, confirm that the latest draft has been published. Instructions in an agent do not grant access to an app by themselves. See ChatGPT Workspace Agents guidance.
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Verify network and tool connectivity in sandboxed runs
When a run uses a sandbox or MCP connection, check which environment originates the connection. An MCP connection made from the executor environment and one made by a remote service have different reachability requirements; verify allowed outbound destinations against the actual connection path. Keep application credentials outside the agent environment where possible, and use appropriately scoped executor credentials. OpenAI’s Agents security guidance covers the relevant security considerations.
Recover safely after a failed or disconnected run
- Record the session ID and retrieve the existing session before submitting more work, especially after an event-stream disconnect.
- Inspect session events and logs, then review the result of each individual tool call.
- Use the turn, session, or environment status to locate the failure, but do not treat a completed turn or idle session as proof that every tool succeeded.
- Retry only after determining whether the original request was rejected, the run failed after starting, or a tool or environment failed.
OpenAI’s Agents SDK session guidance describes retrieving existing session state and saved items. Preserve useful failure evidence, but redact secrets before sharing logs or support material.
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Capture evidence for diagnosis
For a useful handoff or incident record, collect the following details. Redact credentials, tokens, and other secrets.
- Platform, product version, and timestamp.
- Request ID or session ID, HTTP status, and the complete structured error fields.
- Failed turn, session, or environment status, plus any reported parameter name.
- Agent persistence state, authentication principal, and relevant permission scope.
- Connected app and account status, and whether the current draft is published.
- MCP or network connection path and the result of each tool call.
When recurring failures need telemetry
For repeated or production incidents, Codex OpenTelemetry events can help reconstruct prompts, approval decisions, tool results, MCP activity, and network allow or deny decisions. OpenAI describes centralizing these records in SIEM or compliance logging systems as an operational option, not a prerequisite for diagnosing a single malformed request. See Codex OpenTelemetry documentation.
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