Archived
2026-09-19An Empirical Study of Harness Design for Coding Agents
Run-Ze Fan, Zihao Zhang, Simin Ma, Yebowen Hu, Shouju Wang, Kaiqiang Song, et al.
arXiv:2609.20804v1Autonomous coding agents are getting better, but the hidden piece that often determines whether they actually work is the harness around them: the planning loop, the tools they can use, and how much context they keep. This paper asks a practical question with big implications for real software agents: which parts of a coding harness matter most, and when? The authors build a lightweight, fixed execution loop and vary planning, action space, and context management across many matched settings on SWE-Bench Verified and Terminal-Bench. Their main finding is that harness design is not one-size-fits-all. Tight context windows make smart context management crucial, planning helps weaker models more than stronger ones, and bash-only interfaces can be surprisingly effective for models that already know how to use the command line. The takeaway matters because agent performance is not just about model size or benchmark scores; it is about engineering the surrounding system so the model can spend its effort where it counts.
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Previous daily papers
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