An Empirical Study of Harness Design for Coding Agents (arxiv.org)

A recent empirical study has unveiled insights into the design of coding harnesses for autonomous coding agents, highlighting their critical role in translating model capabilities into effective software-engineering outcomes. The research meticulously examines how variations in planning, action space, and context management impact performance across four different models tested on SWE-Bench and Terminal-Bench 2.1. Key findings reveal that tight context management significantly reduces failures, with staging rule-based elision proving to enhance efficiency while avoiding unnecessary complexity. This study is particularly significant for the AI/ML community as it moves beyond monolithic evaluations of coding harnesses, offering a modular framework for assessing individual components. The results suggest that while stronger models benefit from planning as a cost-saving measure, predefined tools can enhance performance for less capable models. These insights pave the way for more tailored harness designs that optimize resource usage, providing a valuable blueprint for future explorations in autonomous coding technologies.