给AI编程助手做“驾驶舱”：组件拆开测，结果反常识
给AI编程助手做“驾驶舱”设计，通常被当成一个整体来评测，好坏说不清是哪块起作用。这篇把执行流程固定，只单独换三块：规划、动作空间、上下文管理，在4个模型上跑了176组对照。结果有几个反常识的点：上下文管理在窗口紧张时价值最大，主要靠防止“上下文溢出”崩溃；先规则删减再让模型总结，效率最高，而让删掉的内容可恢复，模型几乎不用、也没提升准确率；规划对弱模型是准确率拐杖，对强模型反而成了省钱工具；预定义工具帮bash弱的模型，但bash强的模型只用命令行接口就能干，成本还低得多。轨迹分析进一步说明：上下文管理只是延长了执行轨迹，规划改变的是轨迹在哪停，动作空间改变的是写代码的粒度。这些结论不是让你明天就能用上的，但它给了个模块化框架，以后评测编程Agent的每个零件都能对号入座。
📄 原文摘要(英文)
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. 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. We find that: (1) Context management becomes increasingly valuable as the context-window budget tightens, with most of its benefit coming from preventing context-overflow failures. (2) Staging rule-based elision before LLM-based summarization provides the strongest overall efficiency among the context-management strategies, whereas making elided content recoverable adds machinery that models rarely use and yields no accuracy gain. (3) Planning shifts from an accuracy scaffold for weaker models to a cost saver for stronger models, with little change in accuracy. (4) Predefined tools improve performance for models with weaker bash proficiency, whereas bash-capable models can operate effectively with a bash-only interface and achieve substantially lower cost, especially on command-line-centric tasks. 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. These findings inform model- and budget-aware harness design and provide a modular framework for evaluating future harness components.