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To debug an agent that fails only sometimes, preserve the complete execution trace, identify the earliest point where the failing run diverges from an expected path, reproduce that run with every non-model input held constant, and turn the sanitized failure into a regression evaluation. The final answer is evidence of the problem, not a sufficient record of its cause.
Why an agent can fail on one run and succeed on the next
An agent run is a chain of model generations, tool calls, retrieved context, state updates, guardrails and handoffs. A small change at any link can alter every later decision. The visible symptom might be an incorrect answer, a missing tool call or an unsafe action, while the first mistake occurred several steps earlier.
Several variables can change between two apparently identical runs:
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- Retrieval may return a different ordering, score or document set.
- A tool may return a transient error, stale record or different result.
- Conversation state, cache contents, feature flags or configuration may differ.
- A handoff may route the request to another agent or prompt version.
- Timing can expose a race, timeout, retry or partially committed state.
Because later actions depend on earlier observations, comparing only the final response encourages the wrong fix. The practical objective is not to force a bit-for-bit replay of a stochastic model. It is to determine which changing factor tracks with the failure, preserve a representative failing trace, and verify that a change reduces the failure without breaking other behavior.
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Define the failure before trying to replay it
Start with a short incident record. Do this while the user report and service logs are still available.
- User-visible symptom: what the user saw, including the exact text, missing action, wrong value or latency problem.
- Expected behavior: the result, tool call, state transition or safety property that should have occurred.
- Observed behavior: what actually happened, separated from assumptions about why.
- Run or conversation identifier: an ID that lets you join model, tool, application and infrastructure records.
- Time and environment: timestamp with timezone, deployment, model and prompt versions, feature flags, dependency versions and relevant configuration.
- Impact and frequency: affected users or workflows, and how often the symptom has appeared.
Write success as an observable condition. “The agent should be better” cannot be evaluated. “The agent must call the account-status tool before proposing a cancellation, preserve the account ID, and return one of the approved status labels” can be checked. If several answers are acceptable, define the rubric or invariants that all acceptable answers must satisfy.
Capture a complete end-to-end trace
Save the ordered events for the entire execution, not just the last message. At minimum, retain:
- Every model generation, including the prompt or message context supplied to it and the model identifier.
- Tool names, arguments, return values, errors, retries, timeouts and latency.
- Guardrail decisions, validation failures, truncation and fallback behavior.
- Agent handoffs, routing decisions and the state passed between agents.
- Retrieved documents or records, ranking information and the query used.
- Conversation and application state before and after each mutation.
- Timestamps, correlation IDs, deployment metadata and configuration values that affect execution.
- Token limits, decoding settings and other generation parameters when the runtime exposes them.
Use a stable event schema so that traces from different versions can be compared. A useful record gives every span a unique ID, a parent ID, a start and end time, an event type, inputs, outputs, status and redaction status. Store immutable raw events alongside a normalized view used for analysis; otherwise a later parser change can silently rewrite the evidence.
What a trace contains in the OpenAI Agents SDK
The OpenAI Agents SDK is one documented implementation, not a requirement. Its documentation describes traces made of spans and lists runner invocations, turns, agents, model generations, function calls, guardrails and handoffs among the default span types. The SDK documentation states: “The Agents SDK includes built-in tracing, collecting a comprehensive record of events during an agent run: LLM generations, tool calls, handoffs, guardrails, and even custom events that occur.” Use equivalent fields if your stack has its own telemetry.
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Know when tracing is unavailable
Tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy, according to the Agents SDK documentation. If that constraint applies, instrument the application and tools directly, record identifiers and timings, and document which model-level fields cannot be retained. Absence of a provider trace is a telemetry limitation, not proof that the run had no intermediate decisions.
Protect sensitive data in stored traces
Traces can contain credentials, personal data, private documents and tool responses that are more sensitive than the final answer. Redact or minimize payloads before saving or sharing them. Keep a field-level record of what was removed, apply a retention period, restrict access, and separate identifying information from the event stream when possible. A redacted trace is useful only if analysts can still understand the causal sequence, so preserve harmless identifiers, types, lengths, status codes and ordering.
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Find the earliest divergence
Take one failing trace and compare it with an expected path or a successful trace using the same request. Work from the beginning of the run toward the symptom. The first meaningful difference is usually more actionable than the last incorrect output.
- Compare the initial input and context. Check normalization, locale, permissions, conversation history, retrieved context and hidden system state. A missing field or different document can make later model behavior look random.
- Compare retrieval. Record the exact query, filters, corpus version, returned IDs, ranking and truncation. If the failing run received different evidence, investigate retrieval before changing the prompt.
- Compare model decisions. Identify the first generation that differs: a tool choice, argument, refusal, route or intermediate plan. Preserve the full input to that generation, not only its visible text.
- Compare tool execution. Check argument serialization, authentication, response ordering, retries, timeouts and error handling. A correct decision followed by a stale or malformed tool result is a tool or integration failure.
- Compare handoffs and state. Verify agent identity, instructions, conversation variables, memory writes and transaction boundaries. Look for a write that was skipped, duplicated or observed before it committed.
- Compare runtime errors and timing. Align timestamps to expose races, deadline changes, cancellation, rate limits and partial retries. A later wrong answer may simply be the fallback for an earlier timeout.
Label each difference as an input difference, model-choice difference, tool difference, state difference or runtime difference. This classification prevents a prompt edit from being used to mask a data or infrastructure defect.
Build a controlled reproduction
A controlled reproduction is an experiment with one suspected variable changed at a time. Reusing the exact user input is necessary but not sufficient; the surrounding non-model inputs must also be captured.
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- Sanitize and freeze the incident record. Copy the original input, relevant conversation turns, retrieved items, tool responses, state snapshot and configuration into a replay fixture. Replace secrets with stable test values and record every substitution.
- Pin the software and model environment. Use the same application commit, prompt version, model/version identifier, dependency lockfile, tool schemas, retrieval index or corpus snapshot, feature flags and locale. If a provider does not expose a model build identifier, record the model name and request timestamp and state that limitation.
- Stub external tools where appropriate. Return the captured tool responses to isolate agent decisions. Run a second experiment against live sandbox tools to test integration behavior. Do not mix these experiments; a changed tool response makes attribution impossible.
- Set generation controls explicitly. Record temperature or equivalent sampling controls, token limits, seeds when supported, stop conditions and retry policy. A fixed seed, where available, does not guarantee identical output across model or service changes.
- Replay several times. Count successes, failures, timeouts and distinct trajectories rather than declaring a single replay a pass or fail. A repeated run is an experiment, not a guarantee of identical behavior.
- Vary one factor. After establishing a baseline, change only the suspected prompt, model, retrieval result, tool response, timeout, state value or configuration. Keep the fixture and trace schema unchanged so the comparison remains interpretable.
- Measure the first divergence. For each replay, record the first event that differs from the reference and whether the user-visible failure follows. A factor that changes the earliest divergence and the failure rate is a stronger lead than one that changes only final wording.
When exact replay is impossible
Provider-side model updates, unavailable seeds, nondeterministic tool systems and missing intermediate telemetry can prevent bit-for-bit replay. In that case, preserve the original trace, replay all controllable inputs, and report a distribution: for example, the percentage of runs taking each tool path and the percentage violating the success criteria. Do not claim that a replay reproduced the original failure unless the relevant trajectory or symptom actually recurred.
Turn the failure into a regression evaluation
A useful regression case contains a production-like failure and an explicit definition of success. OpenAI’s evaluation guidance recommends moving from individual traces to repeatable datasets and eval runs once “good” is defined. The case should include:
- A sanitized input and the minimum context needed to trigger the behavior.
- Captured or deterministic tool responses when the external system is not part of the test.
- Expected tool calls, argument constraints, state invariants and safety requirements.
- A rubric for semantic quality when multiple answers can be correct.
- Metadata for the originating incident, model and prompt versions, and the suspected failure class.
Use deterministic assertions for structure: required fields, valid JSON, a mandatory tool call, an allowed route, an unchanged identifier or a state transition. Use criteria-based grading and human review for semantic outcomes such as relevance, factual adequacy or tone. Keep the original failing trace as an artifact; the evaluation case is a reusable test, not a replacement for the incident evidence.
Choose the evaluation scope that matches the defect
| Scope | Best for | Repeatability | Typical data source |
|---|---|---|---|
| Single run or step | One tool choice, argument schema, classifier decision or structured output | Fixed assertions are easiest to interpret | Curated test case or one captured trace segment |
| Complete trace | Multi-step planning, retrieval, tool sequencing, guardrails or handoffs | Assertions can check the sequence; rubric grading can assess the final outcome | Captured production trace converted into a fixture |
| Conversation thread | Behavior that depends on earlier turns, memory or accumulated state | Replay requires the full turn history and state setup | Captured multi-turn thread or curated scenario |
LangChain’s LangSmith documentation describes regression testing, backtesting new versions on historical production examples, online evaluation, anomaly detection and production feedback loops. These are examples of implementation patterns; the underlying choice is still to evaluate the step, trace or thread where the defect arises.
Run evaluations continuously
Run the regression set against every prompt, model, tool-schema and retrieval change that could affect the path. OpenAI’s evaluation best-practices guidance recommends continuous evaluation and adding cases as new nondeterministic behavior appears. Track pass rate, failure category, first-divergence location, latency and cost separately. A higher pass rate that hides a new safety violation is not an improvement.
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Evaluate stochastic outputs without overfitting
Exact-string comparison is appropriate only when the output is intentionally fixed. For natural-language answers, judge properties instead:
- Required facts are present and unsupported claims are absent.
- Tool calls use the correct tool and valid arguments.
- Identifiers, totals and units are preserved.
- Safety, authorization and privacy constraints hold.
- The response addresses the user’s request within the allowed scope.
Use several acceptable reference answers or a rubric with explicit pass and fail examples. Calibrate an automated grader against human judgments, sample disagreements, and keep human review for high-impact cases. If a grader can see only the final answer, it cannot detect a wrong tool call that happened to be hidden by a plausible response; provide trace-level evidence when the workflow matters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Separate agent trajectory reproducibility from generated-software reproducibility
Two different questions are often conflated:
- Trajectory reproducibility: whether the same agent request follows the same model, retrieval, tool and state path.
- Generated-project reproducibility: whether software produced by an agent runs in a clean environment with the supplied dependencies.
A 2026 SRI International study found that 68.3% of 300 generated software projects were reproducible without manual intervention: 89.2% for Python, 61.9% for JavaScript and 44.0% for Java. Those figures measure dependency and environment reproducibility of generated projects, not the rate at which an agent repeats a trajectory or avoids nondeterministic failures. Do not use them as evidence that an agent run can be replayed deterministically.
Operational limits and useful context
Observability coverage is improving but incomplete. A LangChain survey page dated June 12, 2026 reports responses from 1,340 participants collected November 18–December 2, 2025: 89% of organizations reported implementing some form of agent observability, 62% reported detailed tracing and 52.4% reported running offline evaluations. These are vendor-published survey findings, not universal population rates. The page argues that nondeterministic agents require rapid iteration to improve quality; treat that as the survey’s framing rather than a guarantee that additional telemetry alone solves failures.
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Common dead ends and their fixes
Looking only at the final answer
Problem: The final text may conceal a wrong retrieval result, skipped tool or fallback path.
Fix: Compare the ordered trace and locate the first divergence.
Rerunning until it “looks fixed”
Problem: A stochastic system can produce a good answer by chance.
Fix: Run a defined number of repetitions, record trajectories and evaluate against a regression set.
Changing several variables at once
Problem: You cannot tell whether the prompt, model, tool or data change mattered.
Fix: Freeze the fixture and vary one factor per experiment.
Using a single golden answer
Problem: Legitimate wording differences are marked as failures, while unsafe but fluent answers may pass.
Fix: Assert structure and invariants, then use a calibrated rubric for semantics.
Saving unrestricted raw traces
Problem: Debug data becomes a privacy and security liability.
Fix: Minimize and redact payloads, document omissions, restrict access and set retention limits.
Assuming a provider trace is complete forever
Problem: Zero Data Retention or another policy may disable tracing, and provider schemas can change.
Fix: Maintain application-level correlation IDs and a documented fallback telemetry path.
A repeatable incident-to-regression workflow
- Open an incident with the symptom, expected behavior, observed behavior and run ID.
- Freeze the original trace and configuration metadata.
- Redact sensitive values while preserving event order and causal identifiers.
- Mark the earliest divergence and classify its likely cause.
- Create a replay fixture with captured non-model inputs.
- Run baseline repetitions and record outcome frequencies.
- Change one suspected factor and repeat the experiment.
- Encode the failure as a step-, trace- or thread-level evaluation.
- Run the case against the proposed fix and the existing regression set.
- Keep the case in continuous evaluation and add variants when related failures appear.
The result is an inspectable record of what happened, a defensible hypothesis about where it first went wrong, and a test that can detect recurrence after the next change.
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