Guide

Evaluation harness guide for agentic AI

If you cannot measure normal cases, edge cases, and prohibited actions before launch, you do not have a production AI workflow. You have an experiment.

Use the Evaluation Template See Workflow Examples
Evaluation stack
Release model
4Core test layers
91%
Target golden-set pass rate
0
Unsafe action tolerance
WeeklyDrift review cadence
100%Release coverage target
FastRollback decision
Direct answer

What an evaluation harness actually does

An evaluation harness is the release discipline for an agent. It tests expected behavior, catches regressions after changes, measures unsafe outcomes, and gives operators a repeatable way to decide whether the workflow can stay autonomous.

Golden set

Representative cases with expected outcomes, including both high-frequency work and known edge conditions.

Release gate

Thresholds that block deployment when accuracy, latency, or unsafe behavior exceed what the workflow can tolerate.

Operating loop

A fixed cadence for reviewing drift, failures, and rule changes after launch so the harness stays current.

Test design

The four layers of agent evaluation

1. Happy path

  • Common case success rate
  • Correct action or recommendation
  • Expected time and cost envelope

2. Edge cases

  • Missing fields and partial context
  • Ambiguous instructions or conflicting evidence
  • Rare but operationally meaningful exceptions

3. Prohibited behavior

  • Blocked actions without approval
  • Data handling outside policy
  • Improper routing, hallucinated tools, or invented facts

4. Human review sampling

  • Blind review of borderline cases
  • Reviewer disagreement tracking
  • Escalation reasons folded back into the golden set
Golden set design

What to include in a production-ready golden set

  • Normal cases that reflect the highest-volume work the workflow will actually see
  • Borderline cases where confidence, routing, or retrieval quality might drop
  • Prohibited actions that should always trigger a stop, gate, or escalation
  • Historical failures and postmortem examples so known regressions stay visible
  • Reviewer labels that tie the expected result back to business policy, not taste

Release metrics that matter

  • Task success rate
  • Unsafe action rate
  • Escalation rate
  • Reviewer agreement rate
  • Latency and cost envelope

Signals that trigger rollback

  • Spike in critical errors or policy violations
  • Drop in golden-set performance after a model or prompt change
  • Material increase in reviewer overrides
  • Unexpected tool misuse or route drift
Operating cadence

How to keep the harness alive after launch

Per change

Run regressions whenever prompts, tools, policies, routing logic, or model versions change.

Weekly

Review drift signals, sampled cases, operator complaints, and threshold misses with the workflow owner.

Monthly

Refresh the golden set, retire stale cases, add new failure modes, and decide whether the workflow can expand in autonomy.

FAQs

Evaluation questions teams ask most

It is the release and monitoring system that tests an agent against representative cases, edge cases, and failure modes before and after changes go live.
Include normal cases, edge cases, prohibited actions, ambiguous cases, and historical failures that matter to the business outcome of the workflow.
Run them on every material change to prompts, tools, models, routing logic, or policy rules, and pair that with a fixed weekly drift review after launch.

Need an evaluation stack before launch pressure hits?

We build the release criteria, golden sets, and review cadence around the actual workflow so production readiness is measurable instead of implied.

Book a Discovery Call See Governance Playbook