OperationsAgent systems topic

Evals and verification

Evals measure behavior across representative tasks; verification checks whether a specific run produced an acceptable result. Production systems need both: aggregate evidence for change decisions and concrete completion checks before outputs or side effects are trusted.

Apply the lifecycleReview sources

Questions to answer

Resolve these before adding tools, frameworks, or automation.

  • Which representative tasks and failure modes define acceptable performance?
  • Which checks can be deterministic and which require expert judgment?
  • How will regressions be attributed to model, context, tool, or workflow changes?

Implementation lifecycle

Build the evidence in this order

  1. 01

    Specify

    Translate desired behavior and known failures into representative examples and graders.

  2. 02

    Measure

    Evaluate the complete workflow, not only isolated model responses.

  3. 03

    Gate

    Require relevant checks before rollout and before consequential side effects.

Artifacts to maintain

  • eval dataset
  • grader
  • acceptance test
  • regression report

Continue through AgentAtlas

Evidence ledger

Primary documentation behind this guide

OpenAI

Evaluation best practices

Eval design, task-specific datasets, graders, continuous evaluation, and avoiding common measurement mistakes.

Anthropic

Demystifying evals for AI agents

Agent-specific evaluation design, outcome and process measures, transcript analysis, and iterative improvement.

Next topic

Observability