In the news
The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents- Google Developers Blog
Google Developers · Published · 3 min read
In 30 seconds
- What happened
- Google outlines behavioral evaluation methods for testing AI coding agents, emphasizing discrete action assertions over end-to-end benchmarks.
- Why it matters
- Engineers building or maintaining agentic systems need this when iterating on prompts, model upgrades, or tool schemas to catch regressions.
- Watch out
- Behavioral evals complement but don't replace end-to-end benchmarks. They work best after agents can dogfood their own codebases, not from day one.
- agent
- eval
The patterns behind this
- Eval-Driven Development (Agent CI)
- Agentic Context Engineering (Evolving Playbook)
- Context Editing & Tool-Result Clearing
Each one covers how the technique works, when it earns its cost, and where it breaks.
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