# harness

A Tsinghua Shenzhen team proposes NLAH (Natural-Language Agent Harnesses): moving agent control logic from code into structured natural language, executed by an IHR runtime. Experiments show harnesses can reshape agent behavior patterns entirely, but more structure doesn't always mean better results. Dan McAteer argues harness engineering matters as much as model capability.

ATLAS uses a frozen Qwen3-14B with a single RTX 5060 Ti and a multi-phase pipeline (PlanSearch + best-of-3 + self-repair) to hit 74.6% on LiveCodeBench — passing Sonnet 4.5's 71.4%. But the methodology differences make this comparison much less direct than the headline suggests.

Ethan Mollick's game-changing AI framework: Model, App, Harness. The same AI (e.g., Claude Opus 4.6) performs vastly differently across layers. Mollick used Claude Code to turn GPT-1's 117M weights into 80 books in ~1 hour, selling out immediately.