Ruiyao Xu

An agent's performance depends not only on its underlying model but on its runtime harness: prompts, memory, skills, tools and orchestration. Harness2 makes harness improvement a two-level recursion. At the task level, a contrastive propose-probe-compose step compares execution behavior before and after edits without ground-truth answers; at the harnessing level, that process is distilled into an experience library that guides later tasks. On WorkBuddy-Bench, JobBench and Harvey's Legal Agent Benchmark, a single step improved both GLM-5 and Gemini 3.5 Flash across four model-harness configurations, by up to 11.5 points, 4.5 points and 11.8 percentage points.

An agent's performance depends not only on its underlying model but on its runtime harness: prompts, memory, skills, tools and orchestration. Harness2 makes harness improvement a two-level recursion. At the task level, a contrastive propose-probe-compose step compares execution behavior before and after edits without ground-truth answers; at the harnessing level, that process is distilled into an experience library that guides later tasks. On WorkBuddy-Bench, JobBench and Harvey's Legal Agent Benchmark, a single step improved both GLM-5 and Gemini 3.5 Flash across four model-harness configurations, by up to 11.5 points, 4.5 points and 11.8 percentage points.