Evaluation
Evaluation
Section titled “Evaluation”Evaluation is the discipline that keeps prompt systems from drifting into anecdote-driven operations. This section focuses on test design, review loops, and ongoing quality control once teams start shipping changes regularly.
Core paths
Section titled “Core paths” Regression loops A practical pattern for protecting quality when prompts, models, retrieval, or workflows change.
How do you evaluate AI agents in production? Use this page when the team needs a production evaluation model that covers outcomes, traces, approvals, and live review.
What should you log for an AI agent in production? Use this page when the team needs a production logging model that supports debugging, evaluation, approvals, and cost control.
What is a good success rate for an AI agent in production? Use this page when the team needs a success metric that respects workflow risk, review burden, and side effects.
Agent evals for tool use Evaluate tool-using agents by plan quality, tool selection, approval behavior, and final outcomes instead of only response text.
Trace grading for tool-using agents Grade the whole run so teams can see where agent behavior fails before the last answer hides it.
Tool selection evals and failure taxonomy Use this page when the team needs to separate missing tool use, wrong tool choice, bad arguments, and approval failures.
Eval datasets for coding agents Use this page when coding-agent evaluation still looks like benchmark prompting instead of repository work.
Approval boundary tests Use this page when approval policy exists on paper but has not yet been validated under realistic agent behavior.
Search evals and citation audits for deep research Use this page when research quality depends on source choice, citation correctness, and escalation discipline rather than polished prose.
EvalOps release gates and scorecard ownership Use this page when evaluation has to become a release system with named owners, real gates, and explicit override discipline.
Shadow evals and canary rollouts Use this page when agent changes need staged release discipline instead of one-shot offline confidence.
LLM graders vs human review Use this page when the team needs a sustainable split between automated grading and reviewer judgment.
Eval-driven development for agentic products Use this page when the team wants evals to shape implementation and release decisions instead of only documenting issues after launch.
Ground truth collection for agent eval ops Use this page when the team needs production-grounded evaluation data instead of benchmark theater or ad hoc examples.
Tool-call success rates and ground truth Use this page when the team needs to separate tool success, workflow success, and final-answer quality in agent evals.
Use cases Good evaluation starts by understanding which mistakes are most expensive in the underlying workflow.
Tooling Choose tooling that supports judgment, not just score collection.
Evaluation questions
Section titled “Evaluation questions”- Which errors are acceptable, and which ones block deployment?
- Which examples should be reviewed by a human every cycle?
- What changes trigger a regression pass?
- How frequently should high-value pages or workflows be re-reviewed?