Continuous evaluation for agents: golden sets, regression testing, red teaming, and quality metrics such as groundedness and citation coverage.

Evaluation

Overview

Agent quality must be measured continuously. Evaluation is the mechanism that turns subjective ‘it feels good’ into objective, repeatable confidence suitable for production change management.

Evaluation building blocks

  • Golden sets: curated queries with expected outcomes and acceptable variance.
  • Task metrics: correctness, completeness, structured-output validity.
  • Grounding metrics: citation coverage, evidence alignment, refusal correctness.
  • Operational metrics: latency, cost, tool error rate, escalation rate.

Where evaluation is applied

  • Pre-deployment: validate new workflows and integrations.
  • CI/CD: prevent regressions after prompt/model/tool changes.
  • Runtime: detect drift (data freshness, tool behavior, user patterns).
  • Security testing: prompt injection scenarios and data exfiltration attempts.

Practical approach

  1. Start with high-impact workflows and define ‘must not fail’ test cases.
  2. Add structured checks (schemas, business rules) before subjective scoring.
  3. Automate nightly eval runs; alert on regressions and cost spikes.
  4. Use failures as feedback to improve retrieval, policies, and tooling.