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Record both the result and the path taken to produce it: model calls, selected tools, arguments, tool responses, guardrails, and handoffs. Because model behavior can vary between trials, look for a repeatable pattern across multiple runs rather than treating one failure as proof.
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Start with the symptom, not a theory
Write the regression as an observable failure before changing anything. “The agent feels worse” is too vague to test. State what worked in the known-good version, what fails now, and how a pass can be checked.
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Define a pass condition for every case
A pass condition may be an exact answer, a required field, a valid format, a specific tool call, a correct argument, a successful database update, or another verifiable environment outcome. For a multi-step workflow, check the external result as well as the agent’s final claim. An agent can say that an action succeeded when the action never happened.
- Instruction failure: a required constraint, format, or policy is missed.
- Answer failure: the response is factually wrong, incomplete, or irrelevant.
- Tool-selection failure: the agent chooses an unsuitable tool or skips a required one.
- Argument failure: the right tool is selected with an invalid, incomplete, or unsafe argument.
- Control-flow failure: a handoff, retry, stop condition, or guardrail behaves incorrectly.
- Environment failure: the external state is wrong even though the final text sounds plausible.
Keep these definitions attached to each test case. They prevent a subjective impression from becoming an untestable blame assignment.
Capture and compare the complete trace
Run one representative failing case on the changed version and the same case on the known-good version. Then inspect the end-to-end record rather than only the displayed answer. OpenAI’s Evaluate agent workflows documentation defines a trace as “the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.” That record shows where the two versions first diverge.
What to inspect
- Model calls: model identifier, messages sent, structured-output settings, stop or retry behavior, and any routing decision.
- Prompt material: system instructions, developer instructions, user input, retrieved context, tool descriptions, and conversation state.
- Tool selection: which tool was called, in what order, and whether a required tool was omitted.
- Arguments: names, types, values, units, filters, identifiers, and any defaults inserted by the runtime.
- Tool results: the exact response returned to the model, including errors, empty results, truncation, stale data, or changed schemas.
- Guardrails and handoffs: policy checks, validation failures, route changes, escalation, retries, and termination decisions.
- External outcome: the actual state change or retrieved fact, independently checked where possible.
The first divergence is a useful localization clue. It is not, by itself, proof of causal responsibility. A tool can return bad data because the implementation changed, or because the model supplied a different argument. A prompt can alter routing, which then exposes a model or tool issue.
Build a repeatable evaluation set
Do not diagnose from a single anecdote. Put the failing examples and representative successful examples into a fixed dataset. Include normal cases, edge cases, ambiguous requests, long contexts, tool errors, and cases that exercise handoffs if those paths matter to users.
Record the expected behavior
For each example, store the input, the success criteria, expected output properties, expected tool calls or arguments when they can be stated precisely, and any environment checks. LangSmith documentation describes curated datasets, reference tool calls, comparisons across application versions, and backtesting on historical examples; the same structure can be implemented with another evaluation system or an internal harness.
Keep the baseline immutable
Save the known-good prompt, model configuration, tool schemas, implementations, routing rules, and grading logic. Tag the exact versions used for the baseline and the candidate. If production data is involved, remove secrets and personal information while preserving the behavior that matters. A moving baseline makes every later comparison ambiguous.
Use the same inputs and conditions
Run the baseline and candidate on identical task inputs with comparable temperature, token limits, context, permissions, tool availability, and grading criteria. If an environmental dependency cannot be held constant, record it as a separate variable instead of silently treating the runs as equivalent.
Change one suspected variable at a time
When several changes shipped together, reconstruct test variants before assigning blame. The practical design is to hold the other configuration steady while swapping one factor, then repeat the comparison for the next factor. This is an experimental method inferred from the need to benchmark prompts, models, tool selection, and arguments; it is not a vendor-guaranteed causal algorithm.
| Comparison | Keep constant | Change | Evidence that supports the hypothesis |
|---|---|---|---|
| Prompt test | Model, tools, routing, inputs, grading, and runtime settings | Known-good prompt versus changed prompt | The regression appears with the changed prompt and disappears when the known-good prompt is restored. |
| Model test | Prompt, tools, routing, inputs, grading, and runtime settings | Known-good model versus new model | Comparable runs on the new model show a repeatable shift in outcomes or traces. |
| Tool test | Prompt, model, routing, inputs, and grading | Tool schema, implementation, endpoint, or returned data | Tool choice, arguments, returned results, or external outcomes change with the tool variant. |
| Orchestration test | Prompt, model, tools, inputs, and grading | Router, handoff, retry, guardrail, or stop logic | The failure begins at a control-flow decision while the underlying model and tools remain comparable. |
If you cannot reproduce the original release exactly, create the closest controlled variants and state that limitation. Do not call a temporal association a cause merely because one change happened before the regression.
Repeat trials when outputs vary
Large-language-model outputs are not always deterministic, even when the input appears unchanged. Anthropic describes each attempt as a trial and notes that outputs vary between runs. Repeat each important case enough to reveal whether the change is systematic, intermittent, or simply noise.
What to report
- Number of trials per version and per test case.
- Passes, failures, and the exact failure categories.
- Observed spread, such as different tool choices or argument errors across trials.
- Runtime conditions that could affect variation, including sampling settings, service changes, or transient tool errors.
A one-run failure is weak evidence of a systematic regression. A repeated shift across representative cases is stronger, especially when the same trace divergence recurs. Do not convert a small, noisy comparison into a universal claim about a model or prompt.
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Use deterministic checks wherever the expected behavior is objective, and rubric-based grading for qualities that require judgment. A single quality score can hide a serious workflow defect.
Deterministic checks
- Exact or schema-valid output format.
- Required fields, values, and ordering.
- Exact tool name and argument structure.
- Permission, authentication, and safety constraints.
- Presence of required steps.
- Final database, file, ticket, or other environment state.
Rubric or semantic checks
- Correctness when several phrasings are acceptable.
- Instruction adherence that cannot be reduced to a string match.
- Relevance, completeness, clarity, and appropriate uncertainty.
- Quality of a handoff or explanation.
LangSmith describes code evaluators, LLM-as-judge evaluators, composite evaluators, and other approaches. Pairwise comparison can be more informative for subjective qualities than pretending that one score has a universal meaning. Evaluate traces as well as final responses: a polished answer can conceal a wrong tool call, an unintended handoff, or an external action that never occurred.
How to recognize a prompt-caused regression
A prompt change is plausible when the changed prompt produces the regression under otherwise comparable conditions and the known-good prompt does not. Inspect instruction following, context placement, wording changes, examples, delimiters, and any prompt-driven routing decision.
Typical trace pattern
The model receives materially different instructions or context, then begins violating a requirement, selecting a different route, or formatting its output differently before any tool implementation difference appears. Confirm that the prompt actually delivered at runtime matches the version you reviewed; templating, truncation, variable substitution, and message-order bugs can make a “prompt change” different from the source file change you intended.
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How to test it
- Freeze the model, tools, inputs, permissions, and grading.
- Run the same dataset with the known-good prompt.
- Run it again with the changed prompt.
- Compare pass rates, failure categories, tool calls, arguments, and handoffs across repeated trials.
- Reduce the changed prompt to the smallest edit that still reproduces the failure, if practical.
If restoring the old prompt removes the failure while every other factor remains controlled, the prompt is a strong candidate. It still may interact with a model or tool change, so test those factors separately before declaring exclusive responsibility.
How to recognize a model-caused regression
A model change is plausible when comparable runs using the new model show a consistent shift in outcomes or traces while the prompt, tools, routing, inputs, and settings remain fixed. Historical backtesting against production examples is one documented way to evaluate a new model release.
Look for distributional changes, not one odd answer
Compare instruction adherence, tool selection, argument precision, handoff correctness, latency or retry behavior when those affect the outcome, and final-response quality. A model may preserve the final answer on simple cases while becoming less reliable on long contexts, ambiguous requests, or tool-heavy workflows. Separate those task classes so a strong easy-case score does not mask a failure in a critical path.
How to test it
- Pin the prompt and every tool version.
- Run the baseline model and candidate model on the same fixed dataset.
- Use multiple trials for variable tasks.
- Grade both the environment outcome and the trace-level behavior.
- Check whether the shift persists across representative categories rather than only one example.
Do not infer that a model is generally worse from a small or noisy sample. Report the cases, trial count, and observed spread so another engineer can judge the strength of the evidence.
How to recognize a tool-caused regression
A tool change is plausible when the selected tool, its arguments, its returned result, or the external outcome changes. OpenAI evaluation guidance identifies tool selection and argument precision as explicit agent-evaluation targets.
Separate selection, arguments, results, and effects
- Selection: the model calls a different tool or fails to call the required one.
- Arguments: the same tool receives a changed identifier, filter, unit, date range, or parameter type.
- Returned data: the implementation, endpoint, permissions, schema, error handling, or freshness changes.
- External effect: the tool reports success but the target state is unchanged or incorrect.
Replay the exact arguments from the known-good trace against both tool versions where safe. Then test the candidate tool with known fixtures or a controlled environment. This distinguishes a tool implementation defect from a model-generated argument defect. Also compare the tool description and schema supplied to the model: changing those is both a tool-surface change and an instruction change.
A practical end-to-end diagnostic procedure
- Write the failure contract. Describe the old behavior, new behavior, pass condition, and affected task categories.
- Preserve artifacts. Save the baseline and candidate configurations, prompts, model identifiers, tool schemas, implementations, inputs, outputs, and environment results.
- Collect paired traces. Capture at least one failing candidate run and a known-good run for the same input.
- Locate the first divergence. Mark the earliest difference in model input, model output, tool choice, arguments, tool result, guardrail, handoff, or external state.
- Assemble the fixed dataset. Include failing examples, successful examples, edge cases, and representative production cases that can be safely retained.
- Run repeated baseline and candidate trials. Keep conditions comparable and record sample size and spread.
- Build isolated variants. Swap the prompt, model, tools, and orchestration separately while holding the rest steady.
- Apply layered grading. Use exact checks for objective behavior and rubrics or pairwise judgments for semantic quality.
- Rank hypotheses. Call a factor causal only when its isolated comparison reproduces the regression and competing explanations have been tested.
- Convert the finding into a guard. Add the failure case to the regression dataset and require the relevant check in future releases.
Worked example: a booking agent starts making wrong calls
Suppose an agent formerly selected a calendar-availability tool, supplied a location and date range, and created a reservation only after receiving an available slot. After a release, users report that it confirms unavailable times.
First trace comparison
The known-good trace selects the availability tool, sends the correct date range, receives an available-slot response, and then calls the reservation tool. The failing trace either calls the reservation tool immediately or sends a malformed date range. That observation narrows the investigation, but it does not yet identify the cause.
Isolated tests
- Restore the old prompt with the new model and tools. If the premature reservation remains, the prompt is less likely to be the sole cause.
- Keep the new prompt and tools but restore the old model. If the old model again requests availability, model behavior becomes a stronger hypothesis.
- Keep prompt and model fixed while replaying the old and new tool schemas and implementations. If the new schema encourages a missing date field or the endpoint returns an empty result as success, investigate the tool surface.
- Check the actual external calendar state. A correct-looking final message does not prove that a valid reservation was made.
Run these variants across multiple booking cases, including unavailable dates, time-zone boundaries, and malformed user dates. The responsible change is the one whose controlled variant consistently reproduces the observed failure, not simply the change that was deployed most recently.
Comparison axes for two agent versions
Use the same representative task set and success criteria for both versions. The following axes cover the workflow from user request to external result.
| Axis | What to measure | Preferred grading approach |
|---|---|---|
| Task success | Whether the requested environment outcome occurred | Deterministic state or API check |
| Instruction adherence | Required constraints, format, policy, and scope | Exact checks where possible; rubric for semantic cases |
| Tool selection | Correct tool and correct order | Trace assertion |
| Tool-argument accuracy | Names, types, values, filters, and identifiers | Schema and value validation |
| Handoff correctness | Whether routing and escalation occur under the intended conditions | Trace assertion plus outcome check |
| Final response quality | Correctness, completeness, relevance, and clarity | Rubric, pairwise comparison, or judge with human review of samples |
For variable outputs, evaluate several trials per case and retain the individual traces. An aggregate score without the underlying failures cannot show whether a change harmed a critical path.
Common diagnostic mistakes
Editing the prompt before reading the trace
A prompt edit may hide a tool error, a routing bug, or a malformed runtime message. Inspect the complete run first so you fix the earliest confirmed divergence.
Blaming the last changed component
Temporal order is not causal proof. A model update can expose a latent schema defect; a prompt update can change routing; a tool response can alter subsequent model behavior. Reconstruct isolated variants.
Comparing different tasks
Baseline and candidate must receive the same representative inputs. Otherwise, task difficulty rather than the software change explains the difference.
Using only final-answer grading
Final text can look correct while the wrong tool was called or no external change occurred. Grade traces and environment state.
Trusting one trial
Variable model behavior makes one run weak evidence. Repeat important cases and report the observed spread.
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If you revise the prompt, model, and tool together, you may improve the symptom without learning its cause. Keep a minimal reproduction and change one factor per experiment.
Turn the diagnosis into a deployment control
Run regression evaluations for changes expected to affect user experience. Monitor production behavior after release, and turn newly discovered failures into dataset examples. LangSmith documents backtesting new versions against historical production examples; OpenAI recommends continuous evaluation and expanding the test set over time.
Before release
- Run the fixed dataset against the release candidate and the pinned baseline.
- Require checks for critical tools, arguments, permissions, handoffs, and external outcomes.
- Review trace samples for new control-flow patterns, not only aggregate scores.
- Record model, prompt, tool, routing, and grading versions with the evaluation result.
After release
- Monitor task success and failure categories in production.
- Sample traces, with sensitive data handled according to your policies.
- Investigate new failures using paired baseline or replay runs where possible.
- Add confirmed failures and important near-misses to the regression set.
- Re-run the evaluation whenever a prompt, model, tool surface, orchestration rule, or grader changes.
This practice turns a one-time debugging exercise into an accumulating safety net. The goal is not to prove that one component is always responsible; it is to make each behavior change explainable, reproducible, and testable.
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