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A useful agent scorecard compares a candidate run with a baseline on the same versioned cases, then shows what changed at both the aggregate and case level. Record the fixture bundle, run configuration, evaluator results, and failure details so a team can tell a real improvement from a regression—or from ordinary run-to-run variation. A seed helps replay controlled sampling, but it does not make an entire model-and-tool workflow deterministic.
What an agent diff should tell you
When an AI agent changes, a single pass rate rarely answers the important question: did it get better, break something, or simply produce a different stochastic outcome? Build the comparison around a curated, versioned dataset and run the baseline and candidate against the same cases. OpenAI recommends datasets and evaluation runs for repeatable comparisons of prompts or agent behavior; LangSmith describes offline evaluation on curated examples and regression comparison across versions.
Show both the overall movement and the cases behind it. An average can hide a critical failure, such as a newly introduced policy violation or a tool call that no longer happens. A scorecard should therefore report sample count and per-case deltas alongside aggregate results.
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Use a run record that preserves enough context to interpret differences later. The following fields form a practical implementation proposal, not a vendor-mandated standard.
- Run identity: agent or build identifier, trace or run ID, and repetition index.
- Agent configuration: model and prompt versions, tool versions, and relevant environment versions.
- Test identity: fixture-set identifier and content digest.
- Replay context: seed, plus a note describing exactly what the seed controls.
- Evaluation context: evaluator versions and per-case outcomes.
- Operational measures: cost and latency, if measured.
- Diagnosis: failure signature for each failed or materially changed case.
Compare several dimensions rather than relying on one headline number:
- Pass rate for required deterministic assertions.
- Task-level success or correctness.
- Expected tool selection and completion of required tool calls.
- Safety or policy violations.
- Latency and cost, when those measures apply.
- Run-to-run variability.
A composite score can simplify a decision, but keep the component results visible. LangSmith documents composite evaluators and comparison views; Promptfoo describes cost and latency thresholds as well as repeated runs.
Keep exact checks separate from judgment
Use rule-based evaluators for properties with explicit, testable conditions: required fields, valid structure, prohibited content, or whether a required tool call occurred. Use an LLM judge when the criterion is subjective, such as tone or semantic correctness. Those are different kinds of evidence and should not be collapsed into an unexplained pass/fail.
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Exact output hashes are useful only for fields expected to be byte-stable. A different wording in semantic text is not, by itself, proof of a regression. For that content, combine deterministic constraints with an explicit semantic rubric, and retain the evaluator version so a changed grader is not mistaken for a changed agent.
Fixture digests identify what was tested
A fixture digest is a content fingerprint for the exact test bundle used in a run. It helps distinguish “same agent, different fixture data” from a change in agent behavior. Record the digest beside the fixture-set identifier in both baseline and candidate results.
There is no universal digest format established by the cited platform documentation. If you implement one, document the digest algorithm and how the fixture contents are canonicalized before hashing; otherwise, differences in ordering or serialization can make identical logical fixtures appear different. Treat this as a local reproducibility convention, not a standard imposed by evaluation platforms.
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Seed replay helps, but does not guarantee determinism
Log a seed and state what it controls. Promptfoo’s CLI documentation describes using a seed to select the same sampled tests, which is useful when reproducing a sampled evaluation. That is narrower than replaying an entire agent workflow exactly: model behavior, tool calls, external services, and the environment can all vary.
Run repeated evaluations when you need to estimate observed variability. Promptfoo’s coding-agent guide recommends repetitions for measuring variance and flexible assertions for equivalent outputs. Repetition reveals how often an outcome appeared in the runs you performed; it does not prove exhaustive reliability or guarantee future behavior.
Best Value
Capture failures as actionable signatures
OpenAI describes trace grading as a way to find regressions and failure modes. As OpenAI’s Evaluate agent workflows documentation puts it: “Graders let you score those traces with structured criteria so you can find regressions and failure modes at scale.” A local failure signature can make that evidence practical to compare and triage; the format below is a recommendation, not a prescribed platform taxonomy.
- Stable failure category: wrong answer, missing or incorrect tool use, malformed output, policy violation, timeout or latency-budget failure, cost-threshold failure, or flaky/intermittent result.
- Fixture ID and fixture digest.
- Run ID and, when available, trace or first divergent step.
- Failed rule or rubric dimension.
- Expected versus observed tool action, where relevant.
- Whether the outcome recurred across repeated runs.
Keep the signature tied to the specific case and run. “Task failed” is too broad to show whether the agent chose the wrong tool, violated an output contract, or crossed a latency budget.
Choose an evaluation setup that fits the workflow
A repository-owned harness gives a team direct control over fixtures, assertions, and data handling, but it also leaves the team responsible for maintaining comparisons and reports. Hosted evaluation and observability platforms can provide datasets, traces, graders, and comparison workflows, while introducing platform and data-handling considerations. OpenAI documents traces, graders, datasets, and eval runs; LangSmith documents offline regression evaluation and online monitoring.
Compare options against the work your team actually needs: dataset and trace support, deterministic and semantic graders, repeated-run support, baseline/candidate reporting, CI integration, data handling, operational effort, and cost. Confirm current availability, terms, and pricing directly with vendors before making a platform decision.
Quick Recap
A practical review sequence
- Freeze the comparison inputs. Select the versioned fixture set, record its identifier and digest, and capture model, prompt, tool, and environment versions.
- Run baseline and candidate. Apply the same cases and evaluation criteria to both; record the seed and what it controls, plus repetition index and run IDs.
- Separate evaluator types. Report exact rule-based checks apart from judgment-based rubric results.
- Inspect deltas by dimension and case. Review task success, tool behavior, safety, latency, and cost where measured, not only a composite score.
- Investigate failures. Attach a stable category and diagnostic context, including trace step and expected-versus-observed action when available.
- Repeat when variability matters. Use multiple runs to observe variance and label the number of runs; do not present that sample as proof of exhaustive reliability.
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