An Empirical Study of Harness Design for Coding Agents
To enable component-level comparisons, we study this question with a lightweight coding harness whose execution loop is fixed while three components are varied: planning, action space, and context management. Across four models evaluated on SWE-Bench Verified and Terminal-Bench 2.1, we evaluate 176 matched settings spanning five context-management strategies, four context-window budgets, and targeted ablations of planning and action space. Trajectory-level analysis explains these effects: context management extends execution trajectories without substantially altering agent behavior, planning changes where trajectories stop, and the action space changes the granularity at which code is written.
- ▪To enable component-level comparisons, we study this question with a lightweight coding harness whose execution loop is fixed while three components are varied: planning, action space, and context management.
- ▪Across four models evaluated on SWE-Bench Verified and Terminal-Bench 2.1, we evaluate 176 matched settings spanning five context-management strategies, four context-window budgets, and targeted ablations of planning and action space.
- ▪Trajectory-level analysis explains these effects: context management extends execution trajectories without substantially altering agent behavior, planning changes where trajectories stop, and the action space changes the granularity at whi
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.20804 |
| Publication time | Fri, 18 Sep 2026 13:06:30 +0000 |
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Computer Science > Artificial Intelligence arXiv:2609.20804 (cs) [Submitted on 17 Sep 2026] Title:An Empirical Study of Harness Design for Coding Agents Authors:Run-Ze Fan, Zihao Zhang, Simin Ma, Yebowen Hu, Shouju Wang, Kaiqiang Song, Fei Liu, Hamed Zamani, Xiaoyang Wang View a PDF of the paper titled An Empirical Study of Harness Design for Coding Agents, by Run-Ze Fan and 8 other authors View PDF HTML (experimental) Abstract:Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic systems, leaving the effectiveness of individual components unclear.
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