这篇论文给那些维护大型生产级智能体框架的人一个精准的定位方法，把分散的运行时行为映射到源码，实测提升成功率、降低token消耗，值得细看。
论文提出Harness Handbook方法，通过静态分析和LLM辅助构建运行时行为到源代码的三级映射。使用BGPD工作流（从系统概览到相关阶段、函数和文件）引导编程智能体进行修改。在Codex和Terminus-2上60个修改请求测试中，规划成功率分别从28.3%提升至38.3%和从26.7%提升至45.6%，计划token使用量下降12.7%和8.6%。所有24个对比中文件级和符号级F1均优于GPT-5.5和Opus 4.8参考计划，完整定位缺失最多降低25.9个百分点。
Great paper on self-improving agent harnesses. (bookmark it) If you maintain a production agent ha...
Great paper on self-improving agent harnesses. (bookmark it) If you maintain a production agent harness, finding every file behind one behavior is often harder than writing the edit. Harness Handbook builds a three-level map from runtime behaviors to source locations using static analysis and LLM-assisted structuring. Its BGPD workflow guides coding agents from the system overview to relevant stages, functions, and files, then verifies every candidate against current source. Across 60 modification requests on Codex and Terminus-2, handbook guidance raised planning win rates from 28.3% to 38.3% and from 26.7% to 45.6%. Planner token use fell 12.7% and 8.6%. File- and symbol-level F1 improved in all 24 comparisons against GPT-5.5 and Opus 4.8 reference plans. Complete localization misses fell by as much as 25.9 points. This is a strong pattern for coding agents that need to evolve large harnesses without losing scattered or rarely executed behavior. Paper: arxiv.org/abs/2607.13285 Learn to build effective AI agents in our academy: academy.dair.ai 💬 2 🔄 2 ❤️ 9 👀 867 📊 4 ⚡