An agent scorecard should answer three questions: did the candidate improve behavior, did it regress on any important case, and could the difference be ordinary run-to-run variation? Compare baseline and candidate builds against the same curated, versioned fixtures; record enough configuration and trace context to interpret each result; and report case-level failures alongside aggregate metrics.
What an agent diff should measure
A single pass rate can hide a serious regression: an agent might solve more cases overall while mishandling a safety constraint or failing to call a required tool on one critical fixture. Track separate dimensions and examine individual case changes as well as totals.
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- Task success or correctness: whether the requested task was completed to the required standard.
- Deterministic requirements: explicit rules such as valid JSON, required fields, or a specified condition.
- Tool behavior: whether the agent selected and completed required tool calls, and avoided disallowed ones.
- Safety: policy or other safety constraints relevant to the task.
- Operational measures: latency and cost when those values are measured and meaningful for the comparison.
- Variability: how outcomes differ across repeated runs of the same configuration.
OpenAI’s agent-evaluation guide describes trace grading as a way to assess workflows and diagnose problems: “Graders let you score those traces with structured criteria so you can find regressions and failure modes at scale.” OpenAI: Evaluate agent workflows
Build the comparison around versioned fixtures
Use a curated dataset of cases rather than a handful of memorable prompts. Each case should have a stable identifier, input, and the expected outcome or evaluation criteria needed to score it. Keep the fixture set versioned so you can tell whether a changed result came from an agent update or from altered test data.
This approach matches the documented evaluation patterns: OpenAI recommends datasets and eval runs for repeatable comparisons, while LangSmith describes offline evaluation on curated examples and regression comparison across versions. See OpenAI: Evals and LangSmith: Evaluation.
Use a fixture digest to identify the exact bundle
A fixture digest is a content fingerprint for the fixture bundle used in a run. It can help distinguish “same agent, different test data” from a genuine behavior change. Record the digest beside a human-readable fixture-set identifier.
There is no universal digest format established by these platform documents. If you implement one, document the algorithm and how the bundle is canonicalized before hashing; otherwise, semantically identical files with different ordering or formatting might produce different fingerprints. Treat the digest as an implementation choice, not a vendor requirement.
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Keep exact checks separate from judgment-based grading
Use deterministic, rule-based checks for requirements that have a clearly testable answer: output structure, required fields, explicit constraints, or whether a specified tool action occurred. These checks are easier to interpret when they fail because the assertion identifies a concrete condition.
Use an LLM judge or another rubric-based evaluator for qualities that cannot be reduced cleanly to exact matching, such as semantic correctness or tone. Keep the rubric explicit and version the evaluator, since a changed judge or rubric can alter scores even when the agent is unchanged. OpenAI and LangSmith document evaluator approaches that include both rule-based checks and model-based assessment; their descriptions are available in the OpenAI Evals guide and LangSmith evaluation documentation.
Do not treat every text change as a regression. Exact output hashes are useful only for fields expected to be byte-stable. For semantic text, combine explicit deterministic constraints with a rubric that evaluates meaning rather than wording alone.
Record enough context to replay and interpret a run
A seed is useful only when you know what it controls. Promptfoo documents a seed for selecting the same sampled tests. That can make test selection repeatable, but it does not guarantee that a multi-step workflow will produce identical results: model behavior, tool calls, external services, and environment state may vary. Record the seed and its intended scope, and use repeated runs to observe variability rather than treating one seeded run as proof of determinism.
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- Agent or build identifier.
- Model and prompt versions.
- Tool and environment versions.
- Fixture-set identifier and digest.
- Seed, what it controls, and repetition index.
- Trace or run identifier.
- Evaluator and rubric versions.
- Per-case outcome and failed assertions.
- Cost and latency, if measured.
- Failure signature and relevant trace detail.
Promptfoo’s CLI documentation covers seeded test sampling. Its coding-agent evaluation guide recommends repeated evaluations to measure variance and flexible assertions where equivalent outputs may differ in wording. Repetition gives an estimate of observed variability; it does not establish exhaustive reliability.
Rank #4
Design the scorecard for decisions, not a headline number
Compare baseline and candidate on the same fixture version and report the sample count. A useful compact view shows both aggregate movement and which cases changed:
| Scorecard dimension | What to compare | What to inspect when it changes |
|---|---|---|
| Required deterministic assertions | Pass rate and failed rules | Exact assertion, fixture, and output or trace evidence |
| Task success or correctness | Per-case outcome and aggregate rate | Changed cases and the evaluator or rubric version |
| Tool behavior | Required tool selection and completion | Expected versus observed action and the first divergent step |
| Safety constraints | Violations and affected cases | Policy dimension and trace context |
| Latency and cost | Measured values or budget-threshold failures | Run conditions and whether measurement was available |
| Run-to-run variability | Outcome spread across repetitions | Repetition count, seed scope, and intermittent cases |
Composite evaluators can help summarize multiple dimensions, and comparison views can make changes easier to scan. Still, retain the underlying measures: a composite score can conceal a critical case-level failure. LangSmith documents evaluator composition and comparison views; Promptfoo describes cost and latency thresholds and repeated runs. See LangSmith evaluation documentation and Promptfoo’s coding-agent guide.
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A failure signature should let someone locate and understand a problem without relying on a vague label such as “agent failed.” A stable category plus diagnostic context is a practical format; the categories below are a recommendation, not a taxonomy prescribed by the cited platforms.
Best Value
- Wrong answer: fixture ID and digest, failed rule or rubric dimension, and the relevant output or trace.
- Missing or incorrect tool use: expected and observed tool action, plus the first divergent step when available.
- Malformed output: failing structural assertion and the affected fixture.
- Policy violation: violated constraint and supporting trace context.
- Timeout or latency budget: applicable threshold and measured result, if collected.
- Cost threshold: applicable budget and measured result, if collected.
- Flaky or intermittent result: outcomes across repetitions, seed scope, and run identifiers.
Include the run ID and trace ID when available, along with evaluator version. Those details help separate an actual agent change from a changed fixture, judge, or execution context.
Choose a harness that fits the workflow
A repository-owned harness can keep fixture definitions and deterministic assertions close to the code and give a team direct control over data handling. Hosted evaluation and observability platforms can provide managed dataset runs, trace views, graders, comparisons, or online monitoring. These are different operational choices, not interchangeable guarantees; compare them against the workflow your team needs.
- Dataset and trace support: Can the system run curated cases and retain enough trace detail to diagnose failures?
- Evaluator options: Can you use exact rules for explicit requirements and rubric-based checks for subjective qualities?
- Repeated runs: Can you measure variation rather than relying on a single outcome?
- Comparisons and reporting: Are baseline-to-candidate and per-case changes visible?
- CI integration: Can the required checks run in your deployment workflow?
- Data handling: Does the way inputs, outputs, and traces are stored fit your requirements?
- Operational effort and cost: What work is needed to maintain the harness or platform, and what terms apply to your use?
OpenAI documents datasets, eval runs, graders, and traces; LangSmith documents offline regression evaluation and online monitoring. Feature availability and commercial terms can change, so check the linked product documentation for the specific service and configuration you intend to use.
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