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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsA 200 OK response means the HTTP request received a successful response; it does not prove that an AI agent completed the user’s task. To find out what happened, check the whole response or stream, the agent turn, each tool execution, the output contract, and the task’s observable result as separate layers.
Why can an AI agent fail after an API returns 200 OK?
HTTP status describes the response to an HTTP request, not the correctness or completion of everything built on top of it. An API can return 200 while the body contains an application-level problem, an agent turn fails, a tool reports an error, or the final output fails to meet the task’s requirements. For a workflow that changes data, even a plausible final answer is not evidence that the requested change actually happened.
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Keep these questions distinct: Did the HTTP exchange succeed? Was the response fully received? Did the agent turn finish successfully? Did the tools do what they were asked to do? Does the output satisfy the contract? Did the user’s requested outcome occur? A positive answer to the first question does not settle the rest.
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How to debug the failure, layer by layer
- Record the HTTP exchange. Capture the status, headers, response body, elapsed time, and provider request ID. A 200 is useful evidence about the HTTP response, not a guarantee about downstream agent work. For the meaning of HTTP status codes, see RFC 9110.
- Consume the entire response. If the API streams events, keep reading until the protocol’s terminal event and handle error events along the way. Anthropic explicitly documents that an SSE stream can produce an error after the API has returned 200. Checking only the initial response headers can therefore miss a failure. See Anthropic’s Claude API errors documentation.
- Inspect the agent turn. Where the API exposes a separate turn or session resource, retrieve it and check its terminal status and error details. OpenAI’s agent error guidance recommends inspecting the failed turn rather than treating the HTTP response as the final verdict. See OpenAI’s Agents API error guidance.
- Check every tool execution. A model’s request to call a function is not the same as the application successfully running it. Log the tool name, arguments, execution result, exceptions, and any required fields that were missing or invalid. The Agents SDK documents tool-call errors among its distinct runtime error categories; see the SDK exception reference.
- Validate both structure and meaning. Parse the response and validate its schema, then apply domain rules separately: for example, whether an identifier exists, a value is allowed, or the caller is authorized. OpenAI’s Structured Outputs guide distinguishes schema-constrained output from JSON mode, which guarantees valid JSON but not adherence to a particular schema. Neither format proves that a well-formed value is true, relevant, or complete.
- Verify the task’s postcondition. Decide what observable fact means “done” for the workflow. For a write action, read back the record or state; for a search, check that required result fields are present; for an answer, apply the agreed evidence or quality criteria. This is an application-level acceptance check, not something the HTTP status can establish.
- Retry only when the evidence supports it. Identify the error class and consider whether repeating the operation is safe before retrying. A repeated request may duplicate a non-idempotent tool action if the earlier attempt succeeded but its result was unclear. Follow provider retry guidance for transient failures, use bounded attempts, and stop if the error changes or the retry limit is reached. Anthropic documents SDK retries for transient errors and use of
retry-afterwhen present; see its API errors documentation. OpenAI’s agent guidance also advises stopping automatic retries when the error changes or the retry limit is reached: Agents API error guidance.
What success means at each layer
| Layer | What success means | What can still fail | What to inspect |
|---|---|---|---|
| HTTP/API request | The request receives a success status. | Application-level error content, missing expected fields, or a later stream error. | Status, headers, body, and request ID. |
| Streaming response | The stream completes according to its protocol. | An error event after the initial 200 or incomplete consumption. | Every event through terminal completion. |
| Agent turn | The turn reaches a successful terminal state. | Failed or incomplete turn, refusal, timeout, guardrail tripwire, or invalid output. | Turn status and structured error. |
| Tool execution | The called function returns a usable result. | Exception, timeout, malformed arguments, or an operation that did not succeed semantically. | Tool input and output, exception, and execution ID. |
| Output contract | The output parses and matches the expected schema. | Correctly shaped but false, incomplete, or irrelevant values. | Schema validation and domain or business validation. |
| User task | The requested outcome is observably true. | No state change, wrong target, partial completion, or an unsupported claim of success. | Read-after-write or another task-specific acceptance check. |
These layers are a practical diagnostic model, not a claim that every provider exposes the same statuses or resources. Use the status and error fields the specific API actually documents.
What agent-specific failures should you look for?
Agent execution adds failure modes that are separate from HTTP transport. The OpenAI Agents SDK, for example, documents invalid model output, refusal, timeout, tool-call errors, and guardrail tripwires as distinct error categories. They are examples from that SDK, not a universal taxonomy for every agent framework. Check the relevant provider or SDK documentation for its own turn states and error fields.
A refusal or a guardrail stop may be an intentional outcome rather than a broken request. A timeout may leave uncertainty about whether a side effect occurred. A tool-call error may mean the model proposed an action but the application could not complete it. Diagnose the recorded state before deciding whether to retry or report completion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why valid JSON can still be a failed answer
Well-formed output solves only a formatting problem. A schema can require fields and constrain their types, but it cannot by itself establish that an ID refers to the right record, that a claim is supported, or that a requested action took effect. Validate structure first, then enforce the business rules and verify the external result.
OpenAI distinguishes function calling, used to connect a model with application tools, from structured response formats that constrain a final response. Its documentation describes JSON mode as producing valid JSON, not guaranteeing schema adherence; Structured Outputs target a supported schema. Neither should be treated as a correctness guarantee. See the Structured Outputs guide.
Quick Recap
Build success checks into the integration
- Log the request ID, status, elapsed time, response body or stream events, agent-turn state, and tool outcomes in a way that lets you trace one task end to end.
- Represent tool failures explicitly in the application rather than converting them into an apparently successful empty result.
- Define acceptance criteria for each task, including required fields and the state that must exist when an action completes.
- Make retries bounded and replay-safe where possible; for side-effecting actions, use idempotency controls or verify state before repeating.
- Return a success result to the caller only after the relevant application-level checks pass. Otherwise return a meaningful failure or incomplete status with diagnostic context.
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