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

Regression loops A practical pattern for protecting quality when prompts, models, retrieval, or workflows change.

AI prompt quality checklist before copying viral prompts Use this page before adopting hot prompts from social, prompt libraries, or creator examples into repeatable workflows.

Coding-agent quality regression playbook Use this page when developers report that an AI coding agent got worse and the team needs evidence, traces, rollback, and prevention.

Coding-agent adoption metrics Use this page when usage dashboards need to separate seats, surfaces, accepted work, review burden, quality, and cost per engineering outcome.

OpenAI Codex code review and PR gates Use this page when Codex output needs PR checks, review evidence, repository evals, and merge discipline.

What is EvalOps for AI teams? Use this page when the team needs a clear model for turning evaluation into release discipline instead of occasional testing.

EvalOps implementation roadmap Use this page when the team needs to move from ad hoc evals into traces, datasets, scorecards, owners, and release gates.

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 should an AI agent audit trail include? Use this page when the team needs a governance-grade record of approvals, evidence, and side effects instead of ordinary debug logs.

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.

How do you monitor AI agents in production? Use this page when the team needs live production signals around failures, approvals, rescue work, latency, and cost.

How to review AI agent production incidents Use this page when incidents need to become eval cases, alerts, release gates, and durable ownership changes.

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.

Production incident response Use this page when evaluation signals need to feed containment, rollback, and incident review rather than static reports.

How should AI teams sample live traffic for agent evals? Use this page when the team needs a live-traffic sampling model that does not miss risky slices or overwhelm reviewers.

LLM graders vs human review Use this page when the team needs a sustainable split between automated grading and reviewer judgment.

Traces vs logs for agent eval ops Use this page when the team needs to separate run-level debugging truth from durable production logging.

AI agent trace retention and sampling policy Use this page when production traces need to support debugging, evals, privacy, audit, and cost control without storing everything forever.

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.

QA scorecards for custom support agents Use this page when a custom support stack now needs a real QA operating model, not just confidence scores.

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.

What should an agent eval scorecard actually measure? Use this page when the team needs a scorecard that reflects outcome quality, tool behavior, review burden, and policy risk.

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

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?