Harness-Bench: Measuring Harness Effects Across Models (arxiv.org)

Researchers have introduced Harness-Bench, a novel benchmark designed to evaluate the effects of different harness configurations on the performance of Large Language Model (LLM) agents across realistic workflows. Traditionally, benchmarks have overlooked the influence of the execution layer—comprising context, tools, and state management—on agent performance. Harness-Bench fills this gap by assessing various harness setups across multiple model backends while maintaining task equivalency, allowing for a clearer analysis of how different configurations impact efficiency, quality, and reliability in agent execution. The significance of Harness-Bench lies in its potential to reshape the evaluation of agent systems in AI and machine learning. By examining over 5,194 execution trajectories and systematically documenting artifacts and usage statistics, the benchmark showcases that agent capabilities cannot be solely attributed to the base model but must also consider the specific harness configuration. This insight highlights common failures in execution alignment, where reasoning diverges from feedback and workspace state, emphasizing the necessity for a more nuanced understanding in developing robust AI agents. Ultimately, Harness-Bench promises to enhance the reliability and transparency of LLM agents, fostering improvements in their practical applications and usability.