How to Build an Evaluation Harness for AI Agents Before Production

This article provides a comprehensive guide to creating an evaluation harness for AI agents to ensure they are production-ready. It emphasizes that agents should only be deployed after passing repeatable tests covering three surfaces: outcome (business action success), trajectory (tool usage and handoffs), and controls (safety, authorization, data handling). The tutorial outlines building a harness with version-controlled test cases, isolated environments, trace recording, deterministic and model-based grading, baseline comparison, and CI/CD release gates. It includes Python/YAML examples and discusses handling nondeterminism, regression detection, and adding production failures to the test suite. The goal is to validate agent safety and utility for specific enterprise workflows, not to create a benchmark.