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“Agent preflight validation failure” is a practical umbrella term, not a universal error category. To find the cause, first identify whether the platform rejected the request, failed during a turn or session, or reported an environment problem. Then follow the error evidence to the relevant request field, identity, workspace setting, tool, or network path. The examples below are specific to OpenAI Agents API, ChatGPT Workspace Agents, and the OpenAI Agents SDK; exact fields and checks vary by product and API version.
1. Identify where the failure happened
Start by separating a rejected request from a failure that happened after the agent run began. The distinction determines which record and details to inspect.
- API request rejected: Check the HTTP status and response
errorobject. - Turn or session failed: Retrieve the failed turn or session and inspect its status and error.
- Environment failure: Inspect the reported environment error rather than treating it as a malformed request.
OpenAI’s Errors and recovery guide describes these distinctions for its Agents API. Other products may expose different records or terminology.
2. Read the structured error before changing anything
For structured API errors, use error.code to guide application logic and error.message to understand the failure. A request-validation error may also include error.param, which can point to the field that needs correction. Not every error includes a parameter, and an application should not assume every code is already known.
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Check the API’s current error documentation for the version you are using before depending on exact code names. Record the full error fields, but redact credentials and other secrets.
3. Check request fields, configuration, and API version
OpenAI’s error guide includes request-level failures involving invalid or oversized input or configuration, an invalid beta header, and invalid tracing endpoint or header values. If the error identifies a parameter, correct that value first. If the error concerns a header, confirm it matches the requirements for the API version in use.
A request can also be accepted at the API boundary but fail later when an SDK validates tool arguments. In the OpenAI Agents SDK, Standard Schema parameters are converted to JSON Schema for the model and validated locally before the tool’s execute function runs. If the top-level request succeeded but a tool did not run, inspect the tool arguments and local validation result; this SDK-specific behavior should not be assumed for other agent frameworks. See the Agents SDK tools guide.
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4. Verify agent identity, credentials, and resource access
Different identity and access failures call for different fixes. In the OpenAI Agents API, agent_not_persisted indicates that the supplied ID belongs to a session-local agent when a saved agent is expected. The documented remedy is to create a saved agent and use its ID. Authentication errors point to credentials; authorization errors point to the caller’s access. For not-found or model-not-found errors, verify that the requested resource or model exists and is available to that caller.
These are examples from OpenAI’s Agents API error guide, not universal error names. Use the documentation for the API version and product that produced the failure.
5. Check Workspace Agents setup when the agent is in ChatGPT
For ChatGPT Workspace Agents, check the workspace configuration separately from the agent’s written instructions. Confirm that the user has the access needed to view, edit, or publish the agent; that the relevant app is enabled; and that its required account connection is in place. If a schedule or API channel is not live, verify that the latest draft has been published.
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An instruction telling an agent to use an app does not itself grant that app access. See OpenAI’s ChatGPT Workspace Agents guidance for product-specific setup details.
6. Check environment and tool connectivity
In a sandboxed deployment, compare the network path the tool actually uses with the destinations allowed by the environment. For MCP connections, determine whether the connection originates from the executor environment or a remote service: those paths have different reachability requirements. A destination reachable from a developer’s machine may not be reachable from the executor, and the reverse may also be true.
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7. Recover safely after a disconnect or failed tool call
Do not submit the same work again just because an event stream disconnected. Save the session ID, retrieve the existing session and saved items, and inspect its events and logs before deciding whether to resume or retry. Review each tool outcome individually: a completed turn or an idle session does not establish that every tool succeeded.
For diagnosis, preserve the session ID and the specific failed turn or tool result. OpenAI’s Agents SDK sessions guide describes session state and recovery concepts; implementation details vary by product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Use telemetry for recurring production incidents
For repeated or production failures, agent-aware telemetry can help reconstruct what happened around a run. OpenAI describes Codex OpenTelemetry events covering prompts, approval decisions, tool results, MCP activity, and network allow-or-deny decisions. These records can be centralized in SIEM or compliance logging systems. Telemetry is an operational option for incident investigation, not a prerequisite for diagnosing a single malformed request.
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See the Codex telemetry guide for the events OpenAI documents.
What to capture before escalating or retrying
Collect enough context to connect the failure to a specific platform layer without exposing secrets. Useful evidence includes:
- Product, API or SDK version, and timestamp.
- Request ID or session ID, HTTP status, and complete structured error fields, with secrets redacted.
- 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, along with whether the current draft was published.
- MCP or network connection path and the result of each tool call.
If a stream disconnected, retrieve the existing session state before sending more work. Never include secrets in logs or support material.
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