HarnessOpt-Bench: A New Benchmark for Evaluating LLMs on Agent Harness Optimization
alex_verem · x · 2026-08-08
A new paper on arXiv introduces HarnessOpt-Bench, a novel benchmark designed to evaluate Large Language Models (LLMs) on end-to-end agent harness optimization tasks.
The paper highlights that an LLM's performance in agentic systems depends heavily on the surrounding harness—prompts, tools, control flow, and orchestration code—not just model weights. The benchmark tasks an optimizer model (an LLM) with iteratively editing a target agent's seed harness within a fixed evaluation budget to improve performance.
Experiments evaluated 5 frontier LLMs across 4 downstream tasks over 111 scored runs. Results show that the variance in capabilities among optimizer models is more significant than the differences among the coding harnesses they operate through.
Related event: ScaleAI Releases HarnessOpt-Bench to Evaluate LLM Agent Optimization(2 posts)→
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