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An Empirical Study of Harness Design for Coding Agents

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TL;DR - This empirical study isolates how planning, action space, and context management affect autonomous coding agents across four models and two software-engineering benchmarks. It shows that effective harness design depends on model capability and context budget rather than a universally optimal configuration.

  • Context management matters most under tight context budgets, primarily by preventing context-overflow failures and enabling longer trajectories.
  • Rule-based elision followed by LLM summarization provides the best overall efficiency; recoverable elisions add complexity without improving accuracy.
  • Planning improves accuracy for weaker models but mainly reduces costs for stronger models, with little accuracy impact.
  • Predefined tools help models with weaker bash skills, while bash-capable models achieve similar effectiveness at substantially lower cost with a bash-only interface.

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An Empirical Study of Harness Design for Coding Agents

arXiv cs.AI Run-Ze Fan, Zihao Zhang, Simin Ma, Yebowen Hu, Shouju Wang, Kaiqiang Song, Fei Liu, Hamed Zamani, Xiaoyang Wang 2026-09-17 arXiv:2609.20804
Public signals Hugging Face upvotes 85
Providers: Hugging Face · Upvotes 85 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:18:35.619847 UTC

TL;DR - This empirical study isolates how planning, action space, and context management affect autonomous coding agents across four models and two software-engineering benchmarks. It shows that effective harness design depends on model capability and context budget rather than a universally optimal configuration.

  • Context management matters most under tight context budgets, primarily by preventing context-overflow failures and enabling longer trajectories.
  • Rule-based elision followed by LLM summarization provides the best overall efficiency; recoverable elisions add complexity without improving accuracy.
  • Planning improves accuracy for weaker models but mainly reduces costs for stronger models, with little accuracy impact.
  • Predefined tools help models with weaker bash skills, while bash-capable models achieve similar effectiveness at substantially lower cost with a bash-only interface.
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