Harness Introduction

Harness is testing infrastructure for AI outputs.

It helps with:

• repeatability
• quality evaluation
• lower testing cost with mocks

Harness Engineering is the quality system around AI workflows. It combines scenarios, fixtures, mock servers, evaluators, traces, regression suites, and failure triage so changes to prompts, tools, models, or code can be verified before release.

Core pieces

Piece	Purpose
Scenario	Defines input, expectation, and acceptance criteria
Fixture	Keeps stable inputs for regression runs
Mock server	Replays downstream success, failure, timeout, and empty data
Evaluator	Checks one quality standard and explains failures
Trace	Records context, tool calls, and model output
Failure triage	Separates prompt failures, tool failures, regressions, and flaky behavior

Harness architecture checklist

• Each scenario has input, expectation, and failure explanation.
• Mock servers cover success, failure, timeout, and empty data.
• Evaluators check one clear standard at a time.
• Fixtures cover normal, boundary, and adversarial inputs.
• Traces make context, tool calls, and model output inspectable.
• Regression suites run before publishing.

Next: Writing Test Scenarios or Evaluation and Quality.