Regression-test the evaluator alongside the agent: keep a versioned suite of representative cases, check graders against trusted human judgments where needed, record a baseline, and rerun the same cases whenever prompts, models, tools, routing, or scoring logic change. Compare results by capability and risk—not just one overall score—and investigate whether each failure comes from the agent, the test, or the grader before deciding whether to ship.
What a useful regression test should catch
An agent can change even when its visible prompt does not. Model updates, tool descriptions, retrieval, routing, guardrails, and scoring logic can all affect outcomes. A regression suite should reveal changes in both the agent’s behavior and the system used to judge that behavior.
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Define the outcomes that matter to users and operations, then organize cases so important slices cannot disappear inside an average. Depending on the system, those slices may include:
- Answer quality: accuracy, relevance, groundedness, style, or reasoning quality.
- Workflow behavior: tool selection, action correctness, handoffs, guardrail behavior, and recovery from tool errors.
- Trust and safety: instruction violations, out-of-scope requests, sensitive data, prompt injection, and compliance.
- Architecture-specific behavior: retrieval and grounding for retrieval-based agents; tool use and action checks for tool-using agents.
OpenAI’s agent evaluation guide describes traces as end-to-end records of model calls, tool calls, guardrails, and handoffs. Grading those traces can help locate workflow regressions that a final-answer score would miss.
Build a representative, versioned test suite
Use real traces, user feedback, known failures, and expert-authored cases. Include ordinary cases as well as edge cases and high-risk scenarios. When production feedback exposes a durable blind spot, add it to the suite so it can be checked in later runs.
Keep each case’s expected behavior or reference value and its provenance. When a result changes, reviewers need to know whether the agent failed, the reference became stale, or the test was wrong. OpenAI’s evaluation guide describes datasets with reference columns that graders can use, and recommends expanding datasets as edge cases and blind spots emerge.
Group tests by capability and risk, and report results for those groups separately. A stable overall pass rate can conceal a serious regression in a small but important category. There is no universal suite size established for production agents; size and coverage should reflect the system’s risks and use case.
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Choose checks that match the property
Use the simplest grader that can validly assess the criterion. A subjective model grader is unnecessary for a determinate requirement, while exact string matching can unfairly fail semantically equivalent answers.
- Deterministic requirements: use exact string checks or programmatic assertions when the expected value is unambiguous.
- Semantic equivalence: use similarity-based checks when wording can vary without changing correctness.
- Subjective qualities or categories: use a model grader with an explicit rubric for a numeric score or label.
- Workflow behavior: inspect and grade traces for tool choice, handoffs, guardrails, and instruction adherence.
- Ambiguous or high-impact cases: involve human annotators or domain experts, and use their judgments to check automated graders.
OpenAI’s agent evaluation guide and evaluation guide describe these grading approaches. A grader’s output is not ground truth simply because it is repeatable: the criteria, rubric, and examples used to validate it matter.
Validate the evaluator, not only the agent
Maintain a set of examples with human-established judgments and run them whenever a model grader, rubric, or scoring pipeline changes. Check whether the automated grader agrees with the human reference, inspect disagreements, and update the rubric or escalate cases that cannot be judged consistently. If the scoring system changes, compare its judgments on the same examples before interpreting a change in the agent’s pass rate as an improvement.
PaperBench illustrates one way to assess a judge: it uses a separate judge benchmark. OpenAI’s 2025 paper describes 8,316 individually gradable tasks across its benchmark rubrics and agent replication tasks based on 20 ICML 2024 Spotlight and Oral papers. These are benchmark-specific figures, not recommended sizes for a production regression suite or proof that a judge is reliable for every task. See the PaperBench publication.
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Run the relevant suite against the current system before evaluating a change. Preserve enough metadata to make later comparisons interpretable:
- Run timestamp and dataset version.
- Agent and prompt versions.
- Model identifier and tool configuration.
- Grader and rubric versions.
- Run settings that affect the result.
Microsoft’s evaluation toolkit describes a versioned, timestamped baseline and per-set pass-rate targets, with hard gates distinguished from softer targets. Set targets and decide which failures block release before examining the changed run; otherwise it is easy to move the threshold after seeing an inconvenient result. Thresholds should reflect the risk and cost of failure in the specific use case. The available guidance does not establish one universal pass rate or statistically adequate sample size.
Rerun the same cases after changes
Use the same versioned cases when changing the prompt, model, tool descriptions, retrieval, routing, guardrails, agent code, grader, rubric, or evaluation data. For a grader or rubric change, include the human-judged examples as well as agent outputs so that a shifted scoring standard does not masquerade as better agent performance.
- Freeze the comparison inputs. Record the current agent configuration, dataset, graders, and run settings.
- Run the relevant suite. Use the same cases and scoring criteria as the baseline unless the change itself is to the dataset or evaluator.
- Compare by slice. Review pass rates for capability and safety groups as well as the overall result.
- Inspect changed examples and traces. Look for altered tool calls, handoffs, guardrail events, and outputs behind score changes.
- Apply the pre-set gates. Block, investigate, or monitor according to the release criteria for each risk category.
- Rerun after fixes. Confirm that the correction resolves the failure without introducing regressions in affected cases.
OpenAI recommends repeatable datasets and evaluation runs for benchmarking changes and comparing prompts over time; traces are useful for debugging workflow behavior. The detailed sequence above is a practical operating process, not a prescribed universal standard.
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Triage failures without blaming the wrong system
For every unexpected result, separate three possibilities: the test or reference is wrong, the grader is inconsistent with the intended criterion, or the agent behavior is actually defective. Microsoft’s toolkit explicitly distinguishes evaluation-setup problems from agent-quality problems.
- Verify the case. Confirm that the input is valid, the reference is current, and the rubric measures the intended behavior.
- Check the grader. Compare its judgment with a human-established answer, especially for subjective or high-impact criteria.
- Inspect the run. Review the trace to locate a mistaken tool choice, failed handoff, guardrail issue, or other workflow change.
- Classify and fix the source. Correct the test or rubric if evaluation is at fault; change the agent if its behavior is wrong.
- Rerun the affected set. Verify the fix against the relevant cases and check neighboring slices for side effects.
Do not discard a failing case merely because its score is inconvenient. If the underlying product behavior or policy has changed, update the reference with a documented reason; if the case exposed a real defect, preserve it as a regression test.
Keep the suite useful over time
Route recurring production feedback, support issues, manual overrides, and known failures into evaluation cases when they represent durable expectations. Periodically review whether cases remain relevant, update references when the product or policy changes, and document why tests were added, altered, or retired. Microsoft describes a production-signal loop through clustering, fixes, and reevaluation; OpenAI recommends expanding evaluation data with newly identified edge cases and blind spots.
Choose an evaluation approach by asking whether it covers the agent’s outcomes and risks, validates subjective graders, exposes workflow traces, supports repeatable versioned runs, fits privacy and CI/CD needs, and produces release decisions tied to operational risk. Those criteria are more useful than treating any single score or tool as a complete measure of quality.
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Product availability can affect a regression process built around a hosted evaluation feature. OpenAI’s dataset guide, viewed on October 7, 2026, stated that existing Evals content would remain available during a transition, become read-only for existing users on October 31, 2026, and that the platform was scheduled to shut down on November 30, 2026. The same guide points to datasets for rapid prompt testing and describes Evals for external-model evaluation, API use, or larger-scale runs. Because these dates and product details can change, check the current official evaluation documentation before planning a new workflow around that service.
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