An Empirical Study of Harness Design for Coding Agents

AI Digest

研究通过实证分析编码代理Harness设计，发现上下文管理在预算紧张时关键，规划对强模型是成本节省工具，预定义工具帮助bash弱模型，bash-capable模型用仅bash界面更高效。

This study reveals how context management, planning, and interface design impact coding agents' efficiency, offering guidelines for budget-aware harness development.

Key points

• 上下文管理在预算紧张时提升效率，主要防止溢出错误 Context management gains efficiency under tight budget constraints
• 规则优先的摘要策略效率最高，可恢复内容无显著增益 Rule-based summarization outperforms recoverable content strategies
• 规划对强模型是成本节省工具，弱模型则作为准确性支撑 Planning shifts from accuracy scaffold to cost saver for stronger models
• 预定义工具帮助bash弱模型，bash-capable模型用仅bash界面更高效 Predefined tools benefit models with weak bash proficiency

Takeaway: Harness组件优化策略可显著提升编码代理性能与成本效率。 / Optimizing harness components can significantly enhance coding agent performance and cost efficiency.

Why it matters 为优化编码代理性能提供实证依据，指导模块化Harness设计，提升开发效率。