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
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Hugging Face upvotes 85
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.