The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents- Google Developers Blog

This post introduces the concept of 'harness engineering' for AI coding agents, focusing on behavioral evaluations as a more reliable measure of confidence than end-to-end benchmarks. It details a paradigm shift from 'report cards' to 'behavioral guideposts', emphasizing discrete, observable actions rather than composite scores. The post outlines a three-step behavioral testing loop: picking one failure mode, writing flexible assertions based on task complexity (strict single-turn for simple tasks, LLM-as-a-judge for complex ones), and automating batch evaluations for stability monitoring. It provides a Python code example using the Antigravity SDK for a behavioral eval and a shell command for running local tests.