1. Why a rubric matters
Teams often know when an agent feels worse, but they struggle to explain why. A rubric turns that intuition into explicit criteria so regressions can be found earlier and discussed more clearly.
2. Break the evaluation into dimensions
Useful dimensions often include factuality, instruction following, tool choice, safety behavior, completion quality, and user experience. Each dimension should have a clear scoring rule rather than a vague description.
3. Standardize scoring language
Rubrics work best when reviewers use the same terms and examples. That means describing what a high, medium, or failing score looks like for each category.
4. Keep automation and human review in balance
Automated evaluations scale well for repeated checks, but higher-impact decisions still benefit from human grading on representative traces. The goal is to automate coverage without losing judgment quality.
5. Use the rubric as a release regression detector
The most practical use of a rubric is not academic benchmarking. It is release protection. If the same tasks suddenly score worse after a change, the team gets a concrete signal before users feel it.
Practical Checklist
- Score the agent across dimensions that map to real product risk, not abstract elegance.
- Define scoring anchors so reviewers interpret quality consistently.
- Use the rubric for release regression detection, not just occasional research review.
References
- OpenAI Evals Guide
Current guidance for structured agent evaluation.
- Anthropic Evaluation Tooling
A current reference on evaluation workflows.
- METR, Evaluations
A useful external reference for more formalized evaluation thinking.