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When an AI agent fails only on some runs, the final answer is rarely enough to explain why. The reliable approach is to preserve the entire execution, locate the first divergence from the expected path, reproduce that divergence under controlled conditions, and turn the sanitized failure into a regression evaluation. Repeating the same prompt can reveal a pattern, but it cannot by itself prove that a run is reproducible or identify the changing cause.
What to do first when an agent fails only sometimes
Start a failure record before changing prompts, tools, or code. Write down the user-visible symptom, the behavior you expected, what actually happened, and the run or conversation identifier. Include the time, deployment or environment, and any incident context that could distinguish one execution from another.
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- Symptom: what the user saw, such as an incorrect answer, a missing tool call, an invalid structured response, a bad handoff, or a state update that never occurred.
- Expected behavior: the observable result and any required intermediate action.
- Observed behavior: the exact output or state difference, not a paraphrase.
- Identifier: the run ID, trace ID, thread ID, or conversation ID that lets another engineer retrieve the execution.
- Timing and version: timestamps, deployment, model and model-version setting when available, code revision, dependency versions, and relevant configuration.
Do not begin with the last message alone. An agent can produce a plausible final response after an earlier retrieval miss, tool error, incorrect argument, omitted handoff, or state mutation. Those upstream events are often the real defect.
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A useful trace preserves the ordered events that produced the result. At minimum, save model generations, tool calls with their arguments, tool results, guardrail decisions, handoffs, retrieved context, timestamps, and the version or configuration metadata available at runtime. Keep the causal order explicit so an engineer can see which observation preceded each decision.
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What belongs in a trace
- Inputs: the user message, system and developer instructions, conversation history, and other inputs supplied to the run.
- Context: retrieved documents or records, memory, feature flags, authorization context, and other non-model data.
- Model events: every generation, including structured output, tool-selection decisions, finish reasons, and decoding settings when the runtime exposes them.
- Tool events: function name, arguments, response, latency, status, retries, timeout, and error payload.
- Control-flow events: guardrail checks, agent handoffs, planner or router decisions, loop exits, and state reads or writes.
- Environment: deployment, code revision, dependency versions, region where relevant, and configuration that can change behavior.
- Identifiers and timing: trace, span, run, and thread IDs plus timestamps or durations that allow events to be ordered and correlated.
The OpenAI Agents SDK documentation describes this model as traces made of spans and lists runner invocations, turns, agents, model generations, function calls, guardrails, and handoffs among its default span types. Its documentation also says built-in tracing collects a comprehensive record of LLM generations, tool calls, handoffs, guardrails, and custom events. That SDK is one implementation, not a requirement: the same causal information can be captured by another observability system or by application-level instrumentation.
Make the record inspectable
Store structured events rather than one large text blob. A simple event shape can include an event type, start and end time, parent span, input and output payloads, status, and metadata. Keep the original ordering even when events are exported to a system that displays them as a tree. Preserve failed tool responses and exceptions; removing them can make a broken path look successful.
{"trace_id":"t-1842","thread_id":"th-77","events":[{"type":"model_generation","span_id":"s1","input_context_hash":"...","output":"...","model_config":{"temperature":"recorded-if-available"}},{"type":"function_call","span_id":"s2","name":"lookup_record","arguments":{"id":"redacted"},"result":{"status":"error","message":"..."}},{"type":"handoff","span_id":"s3","from":"agent_a","to":"agent_b"}],"environment":{"revision":"..."}}The values in this example are illustrative. The important property is that a reviewer can reconstruct the path and identify which input, observation, or decision changed.
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Find the earliest divergence
Compare the captured action sequence with the path that should have occurred. Start at the beginning and mark the first point where the actual run differs. A later bad answer may be a consequence of that earlier difference, so fixing the last visible error can hide the root cause.
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Use this comparison order
- Inputs and context: Was the user message identical? Did conversation history, memory, feature flags, authorization, or retrieved documents differ?
- Retrieval: Did the same query return the same records, ordering, scores, and metadata? A changed or empty result can alter every subsequent generation.
- Tool selection: Did the model choose the expected function or handoff? Check the exact tool schema and arguments.
- Tool output: Did the tool return the same data, error, latency behavior, or timeout? Confirm whether retries changed the response.
- State: Did a prior turn or tool write different state, or did a concurrent update race with the run?
- Guardrails and routing: Did a policy check, router, or handoff send execution to a different agent?
- Runtime failure: Did an exception, quota response, cancellation, or network error interrupt the expected path?
- Generation: Only after the non-model inputs match should you treat a different model choice or continuation as the first divergence.
Annotate the trace with the first mismatch and the evidence for it. “The final answer changed” is an outcome; “retrieval returned an empty set at span 4, so the agent skipped the required lookup” is a diagnosis that can be tested.
Build a controlled reproduction
Replay the exact user input and captured non-model inputs whenever possible. The goal is not to force identical text on every attempt; it is to isolate which changing factor tracks with the failure.
Freeze the variables that matter
- Use the same code revision and prompt or instruction set.
- Pin the model and model version when the service exposes a version.
- Reuse the captured tool responses, retrieval results, and guardrail decisions for a first-pass replay.
- Restore the relevant conversation state and memory snapshot.
- Match configuration, decoding settings, timeouts, retry policy, and feature flags.
- Run in an environment with the same dependency versions and external-service contracts when runtime behavior is part of the issue.
Save a manifest with each replay. It should identify the original trace, the frozen inputs, the variables intentionally changed, and the resulting trace ID. Without that record, a series of reruns is difficult to interpret.
Vary one suspected factor at a time
After a frozen replay, change one variable: for example, restore live retrieval while keeping the model response and tool outputs fixed, or use the original retrieval while changing the model configuration. Record whether the earliest divergence moves. If changing retrieval recreates the failure while all other inputs remain fixed, retrieval is a stronger suspect than an unexplained “model randomness” label.
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A repeated run is an experiment, not a guarantee of identical behavior. Sampling, service-side changes, timing, concurrency, and unrecorded context can still produce different trajectories. Treat each run as evidence and report the conditions under which it was obtained.
Use recorded responses before live services
For the first diagnosis, replaying captured tool and retrieval responses removes external variability and makes the model’s decision path easier to inspect. Then add live dependencies back one at a time. This separates an agent-policy problem from a tool, data, or infrastructure problem and prevents a flaky downstream service from obscuring the causal sequence.
Turn the failure into a regression case
Once the earliest divergence is understood, create a sanitized test case from the production-like failure. Include the user input, the relevant context, the expected behavior, and the condition that made the run fail. State what success means in terms that can be evaluated after a code, prompt, model, or tool change.
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- Input: the original request with personal, confidential, and unnecessary fields removed.
- Context: only the retrieval records, state, and tool fixtures needed to reproduce the decision.
- Expected behavior: required tool calls, valid structure, state invariants, policy outcome, or a semantic rubric.
- Failure signature: the earliest divergence and the user-visible consequence.
- Provenance: the original trace ID, capture date, software revision, and any transformations applied during sanitization.
OpenAI’s evaluation guidance recommends moving from individual traces to repeatable datasets and evaluation runs once the team knows what “good” looks like. Its best-practices material also recommends continuous evaluation and adding cases as new nondeterministic behavior appears. A regression dataset should therefore grow from real failures instead of containing only hand-written happy paths.
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Choose the evaluation scope
| Scope | Use it when | What to assert | Strength and limitation |
|---|---|---|---|
| Single run or step | The defect is one tool choice, argument, or structured response. | Exact schema, required field, function name, argument, or state invariant. | Easy to interpret and fast to run; it may miss failures caused by earlier context. |
| Complete trace | The outcome depends on retrieval, routing, tools, retries, or a multi-step workflow. | Ordered events, allowed transitions, tool results, guardrail decisions, and final outcome. | Reveals workflow-level regressions; requires stable fixtures and more instrumentation. |
| Conversation thread | Behavior depends on earlier turns, memory, or accumulated state. | Cross-turn state, later responses, handoffs, and invariants over the whole thread. | Captures context-dependent defects; is more expensive and harder to minimize. |
LangChain’s documentation describes backtesting new versions on historical production examples and using failed runs to improve offline datasets. That approach is useful when a defect only appears in realistic context. Evaluate at the smallest scope that contains the cause, then add a broader test if the failure also depends on preceding events.
Combine deterministic checks with semantic grading
Use deterministic assertions wherever the requirement is exact: a tool must be called, an argument must satisfy a schema, a handoff must occur, or a state field must not be lost. For answers with several acceptable phrasings, use criteria-based grading and human review rather than exact string matching. Keep the rubric explicit about safety, completeness, factual support, and prohibited behavior.
Trace grading is useful during diagnosis because it can score intermediate events, not only the final message. Dataset and evaluation runs are better for comparing a changed system across many cases. Keep the original failing trace linked to the regression result so a test failure leads back to evidence rather than an opaque score.
Operate the workflow continuously
After the immediate fix, run the regression case against every relevant prompt, code, model, tool, and retrieval change. Monitor production for new anomalies and feed confirmed failures back into the dataset. Separate a test that guards one exact step from a test that measures an end-to-end outcome; their failure messages should tell the engineer which layer changed.
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Suggested incident-to-regression workflow
- Open an incident record with symptom, expected result, observed result, and identifiers.
- Export the complete trace and verify that model, tool, retrieval, guardrail, handoff, state, and timing events are present.
- Mark the earliest divergence against an expected path.
- Sanitize and fixture the non-model inputs needed for a controlled replay.
- Replay with all variables frozen, then vary one suspected factor.
- Fix the earliest causal defect rather than only rewriting the final response.
- Create a regression case with a clear assertion or rubric.
- Run the case and neighboring cases in an evaluation run before deployment.
- Keep the case in continuous evaluation and review it when system behavior or requirements change.
Protect data and account for observability limits
Traces can contain the same sensitive information as the agent itself: personal data, credentials accidentally placed in tool arguments, private documents, and internal system instructions. Redact or minimize payloads before storing or sharing them. Document which fields were removed, the retention period, access controls, and whether the sanitized fixture still represents the original decision.
Do not assume that an observability system is available in every deployment. The OpenAI Agents SDK documentation states that tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. If built-in tracing cannot be used, capture the minimum equivalent event record in an approved system, subject to the organization’s privacy and retention requirements. Also record missing telemetry as a limitation; an incomplete trace should not be presented as proof that an event did not occur.
Do not confuse agent nondeterminism with software-project reproducibility
Two statistics often appear in discussions of reproducibility but measure different problems. A 2026 SRI International study reported that 68.3% of 300 generated software projects were reproducible without manual intervention in clean environments with their supplied dependencies; the reported rates were 89.2% for Python, 61.9% for JavaScript, and 44.0% for Java. Those figures concern whether generated projects run with their dependencies, not whether an agent follows the same trajectory on repeated executions.
Agent-failure debugging instead asks whether a particular execution can be inspected, which input or event first changed, and whether the failure can be detected after a system change. Do not use the project-reproducibility percentages as failure rates for agents, and do not claim that a captured trace guarantees bit-for-bit replay.
What current adoption data does—and does not—show
A LangChain survey page dated June 12, 2026 reported responses from 1,340 organizations collected from November 18 to December 2, 2025. It said 89% had implemented some form of agent observability, 62% had detailed tracing, and 52.4% reported running offline evaluations. These are vendor-published survey findings, not universal population rates, and the categories may overlap. They do show why trace capture and evaluation are becoming standard engineering concerns, not substitutes for a diagnosis in a particular system.
Common failure patterns and the right response
| Observed pattern | Likely first check | Useful corrective action |
|---|---|---|
| The same request sometimes skips a tool. | Compare model context, tool schema, tool availability, and decoding configuration at the first decision span. | Fixture the context and assert the required tool call or a safe refusal when the tool is unavailable. |
| The tool is called, but arguments are occasionally wrong. | Inspect the exact generation and schema presented at that turn; verify whether retrieval or prior state changed. | Add structured-argument assertions and a case covering the production context. |
| Retrieval appears empty or inconsistent. | Compare corpus version, filters, permissions, query, ranking, and returned ordering. | Record retrieval results and replay them as fixtures before testing the live index. |
| Only long conversations fail. | Compare thread state, truncation, memory writes, and earlier handoffs. | Evaluate the full thread and assert state invariants across turns. |
| Failures correlate with timeouts or retries. | Inspect latency, retry count, partial responses, and duplicate side effects. | Replay the failure with recorded timing or injected tool outcomes, then test idempotency and timeout handling. |
| The final answer is wrong but every visible step looks plausible. | Look for a subtle earlier retrieval, authorization, or state difference rather than only grading the final text. | Grade the trace and add an assertion at the earliest meaningful decision. |
A practical checklist
- Have you saved the exact input, expected result, observed result, and identifier?
- Can another engineer inspect every model generation, tool call and result, retrieval response, guardrail, handoff, state change, and error in order?
- Are model and configuration details recorded when available?
- Have you compared the actual trace with an explicit expected path?
- Can you name the earliest divergence rather than only the final symptom?
- Did you freeze non-model inputs before varying one factor?
- Are sensitive fields redacted, and are omissions documented?
- Is the regression case tied to a production-like failure with a clear definition of success?
- Does the test scope match the defect: step, complete trace, or conversation thread?
- Will the case run continuously after the fix?
Bottom line
The dependable unit of investigation is the execution trace. Capture the whole path, identify the first divergence, replay it with controlled inputs, and encode the result as a regression case at the scope where the defect occurs. This process makes intermittent failures testable without pretending that stochastic model behavior or changing services can always be replayed exactly.
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