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A 200 OK response proves only that a server completed an HTTP exchange. It does not prove that your LLM chose the right tool, used current evidence, followed a schema, obtained approval, completed a side effect, or answered the user correctly. The five bugs below are silent because each can produce a normal-looking response while damaging the workflow behind it. Fix them by treating correctness as a separate, testable outcome: validate every model result, log and evaluate retrieved context, assert tool and approval decisions, make state transitions idempotent, and capture an end-to-end trace. Then turn each production failure into a repeatable evaluation case so a later prompt, model, retrieval, or infrastructure change cannot reintroduce it.
Why can an LLM request return 200 while the task still fails?
Transport success and task success are different signals. An HTTP client, proxy, or provider can correctly deliver a response even when the model’s content is wrong or an agent took an unsafe path. A non-empty string is equally weak evidence: fluent text can contain stale facts, violate policy, omit required fields, or describe an action that never happened.
| Silent bug | What looks healthy | What is actually broken | First useful check |
|---|---|---|---|
| Semantic success treated as transport success | HTTP 200 and plausible prose | The answer, tool choice, grounding, or policy result is wrong | Grade the trace against task requirements |
| Parser or schema fallback | Defaults, an empty list, or coerced values | The model output did not match the contract | Run strict validation and record failures |
| Retrieval/context defect | Confident answer with citations or snippets | Evidence is stale, irrelevant, incomplete, or adversarial | Inspect document IDs, versions, scores, and filters |
| Tool or workflow execution bug | Assistant confirms an action | Wrong tool, skipped approval, duplicate side effect, or incomplete turn | Compare intended and completed state transitions |
| Timeout or observability blind spot | No provider error, or only an aggregate latency alert | A client-side timeout, retry, or missing trace hid the failure | Correlate request IDs and percentile timings end to end |
LangChain summarizes the risk plainly: an agent can look healthy in production and still fail the task. The remedy is to instrument the task, not just the transport.
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The first bug is using infrastructure signals as a quality gate. Your handler sees status 200, parses a string, and marks the request successful. The model may nevertheless select the wrong tool, retrieve old context, skip a required approval, or produce a plausible explanation of an error.
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What to measure instead
- Task completion: Did the requested outcome occur, rather than merely receive a reply?
- Grounding: Are material claims supported by the permitted context?
- Policy: Did the response obey safety, privacy, and business rules?
- Tool choice: Was the selected function appropriate, with the right arguments?
- User usefulness: Did the result satisfy the acceptance criteria for this request?
OpenAI’s trace-grading guidance treats these as explicit criteria. A grader can label a run for an incorrect tool, a policy violation, or a regression after a prompt or routing change. The important design choice is to keep those labels separate from HTTP status.
How to expose the mismatch in code
Return a structured application result with an outcome such as completed, needs_review, or failed_quality_check. Keep the original transport status for operations, but do not let it set the business success flag. Emit the quality decision, the rule that produced it, and the trace ID in the same record so support staff can find the evidence.
How can malformed model output look successful?
A model can produce syntactically valid text that violates the structure your application expects. Permissive coercion, truncation, default values, or code that returns an empty list after a parse exception hides the defect and gives downstream code something that looks usable.
Validate every structured response
- Define a versioned schema with required fields, allowed values, length limits, and cross-field rules.
- Parse the raw model message without silently repairing it.
- Run schema validation and business-rule validation as separate stages.
- Record the raw response, parser outcome, validation errors, and schema version in the trace, subject to your privacy policy.
- Route invalid results to an explicit failure or human-review state; never convert failure into an empty success value.
For example, a triage result might require a ticket ID, a priority enum, and at least one evidence reference. A response with a misspelled priority should fail validation, not become a default “low” ticket. If a fallback is necessary for availability, expose a distinct status and prevent it from triggering side effects.
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Why exact-match tests are not enough
OpenAI’s evaluation guidance recommends datasets and explicit graders for output criteria. Exact string matching misses acceptable wording changes and fails to test whether an output is grounded, complete, or safe. Use deterministic checks for fields and invariants, plus graders for semantic requirements; retain the failing example in the dataset.
What retrieval and context defects make a fluent answer wrong?
Retrieval is part of the application, not a neutral prelude to generation. A retriever can return stale, irrelevant, incomplete, or adversarial material. The model may then answer fluently while relying on the wrong evidence.
Log enough context to reproduce the run
- Document or chunk identifiers and source versions
- Retrieval query after normalization
- Scores, rank, filters, and the number of candidates considered
- Index or embedding-model version
- The exact context delivered to the model, with secrets redacted where required
- Whether a document was user-supplied, system-approved, or externally fetched
Microsoft’s agent guidance warns that retrieved data can carry indirect prompt injections and that LLM output can be plausible yet factually incorrect. Treat retrieved text as untrusted input: keep it separate from trusted instructions, constrain which fields can influence tool calls, and test documents that attempt to override the system’s rules.
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Separate retrieval failures from generation failures
Replay the same prompt with the captured context. If the answer becomes correct when you replace the retrieved chunks, the defect is retrieval or indexing rather than generation. Conversely, if the model still fails with verified evidence, inspect the prompt, schema, and grader. This split prevents teams from endlessly changing prompts to compensate for an outdated index.
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Why do tools and workflows fail silently after the assistant replies?
Agent workflows can be wrong even when the conversational turn is normal. Common cases include selecting a similar but incorrect tool, skipping an approval gate, applying a side effect twice after a retry, or losing an asynchronous continuation. The assistant’s confirmation is not proof that the business state changed.
Model the workflow as explicit state
- Record the requested operation and a unique operation or idempotency key.
- Represent states such as
requested,awaiting_approval,approved,executing,completed, andfailed. - Persist the state transition before and after each side-effecting call.
- Require an assertion that the selected tool and arguments match the allowed operation.
- Make retries consult the stored key and state so an already completed action is not executed again.
Approval must be a verifiable event, not a phrase in the model’s output. Store who or what approved the action, when, under which policy version, and which exact arguments were approved.
Recovering an uncertain turn
OpenAI advises checking status, saved state, completed actions, and error codes before retrying because a session can fail after an earlier response succeeded. If state is uncertain, reconcile with the tool’s source of truth first. Only then retry an operation that is safe and idempotent; otherwise send it to review rather than guessing.
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A client, proxy, load balancer, or stream can time out before the provider receives the request. The provider dashboard may therefore show no matching error. Retries can create a second request while the first is still running, making the incident look like an intermittent model problem.
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Trace the complete request path
Capture one correlation ID from the user’s request through gateway, model call, retrieval, tool spans, and final response. For every span record:
- Model, prompt, tool, and retrieval versions
- Request and provider IDs
- Input and output token counts
- Queue, connection, model, tool, and total durations
- Timeout limit, retry count, and retry reason
- Validation, policy, and grader results
Analyze P50, P75, and P95 latency by route and dependency rather than relying on an average. A healthy average can conceal a long tail that regularly exceeds a user’s deadline. Compare the timeout boundary at each hop and label whether the provider ever acknowledged the request.
Make streaming failures visible
For streamed responses, record whether headers arrived, how many tokens were received, whether the stream ended normally, and whether the application committed a partial answer. A partial stream must not be mistaken for a completed structured result; either validate the assembled message or mark it incomplete.
How do I debug one silent failure from production?
Start with one failing user request and follow the evidence in order. This keeps a broad “the model is unreliable” complaint from turning into random prompt edits.
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- Pull the end-to-end trace. Confirm the correlation ID, timestamps, route, model, prompt version, and deployment version.
- Check the contract. Inspect raw output, parser errors, schema version, business-rule results, and any fallback branch.
- Inspect context. Compare retrieved IDs, versions, scores, filters, and delivered text with what the request required.
- Audit orchestration. Verify tool selection, arguments, approval events, state transitions, retries, and idempotency keys.
- Reconcile external state. Check the system that owns the side effect instead of trusting the assistant’s confirmation.
- Analyze timing. Locate the first timeout, cancellation, queue delay, or dropped stream across every hop.
- Reduce the case. Put the request, context, expected properties, and observed failure into a small evaluation dataset.
- Add a targeted grader. Encode the violated requirement, such as “must cite the current policy” or “must not call the refund tool without approval.”
- Fix the responsible layer. Change retrieval, schema handling, orchestration, prompt, or timeout configuration based on the evidence.
- Rerun before release and retain the case. Keep the regression example permanently so a later model or prompt update cannot silently bring the defect back.
How should teams compare fixes?
No single control catches all five bugs. Compare a proposed fix on the dimensions below before making it a production gate.
| Dimension | Question to ask | Evidence of a strong fix |
|---|---|---|
| Coverage | Which failure modes does it test? | It checks semantics, structure, grounding, policy, tools, and state where relevant |
| Reproducibility | Can another engineer replay the bad run? | The trace contains versions, context, tool spans, timing, and IDs |
| Latency and cost | What does the control add to each request? | Cheap deterministic checks run inline; deeper graders run asynchronously when safe |
| Blast radius | Can a fallback cause harm? | Invalid or uncertain results cannot trigger irreversible side effects |
| Operational ownership | Who reviews failures and updates tests? | A named team maintains graders, datasets, thresholds, and escalation paths |
OpenAI describes a progression from inspecting individual traces to repeatable datasets and evaluation runs. Use that progression deliberately: traces explain a failure, while a maintained dataset tells you whether a fix survives the next change.
What should be in the production checklist?
- HTTP status is recorded separately from task-quality status.
- Every structured output is validated without silent coercion or empty-success fallbacks.
- Grounding, safety, completeness, and tool-choice checks are explicit.
- Retrieved material is versioned, logged, and treated as untrusted input.
- Side effects use idempotency keys, persisted states, and approval assertions.
- Retries inspect prior state before repeating an operation.
- Client, proxy, provider, and stream timeouts are visible in one trace.
- Latency dashboards show percentile distributions by route and dependency.
- Prompt, model, schema, retrieval, and tool versions are attached to each run.
- Every confirmed incident becomes a replayable evaluation case with a grader.
Frequently Asked Questions
Should I retry an LLM response that looks wrong?
Not automatically. First classify the failure as transient transport trouble, invalid output, bad context, or an unsafe workflow state; retrying a semantic failure can repeat the same defect, and retrying a side effect can duplicate it. Reconcile stored state and the external system before deciding whether a retry is safe.
What is trace grading in an LLM application?
Trace grading assigns labels to a complete run rather than judging only the final text. A grader can inspect retrieval, tool choice, approval, policy compliance, and the final outcome, making regressions visible even when wording changes.
How can I test a prompt change before deploying it?
Run the changed prompt against a fixed dataset containing normal requests and retained failure cases. Compare schema-validity, grounding, policy, tool-choice, and task-completion graders with the previous version, and block the release when a critical case regresses.
How much model output should application logs retain?
Retain the fields needed to reproduce and grade a run, but apply your privacy and retention rules: redact secrets and personal data, keep stable document and version identifiers, and record validation errors and hashes when raw text is unnecessary. The trace should still show which context and instructions were used.
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