让大模型自动总结重复任务为可复用流程，提升效率与成功率
From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
- 将基础操作组合成可调用的标准化流程（SOP）
- 实验显示任务成功率显著提升，交互轮次大幅减少
- 适合需要长期优化和自主演化的智能体开发者
工具使用使大型语言模型（LLM）代理能够与现实世界交互并解决复杂任务。然而，现有代理框架大多依赖由细粒度原子动作（如基本文件读写或单轮搜索）组成的静态工具集，导致代理在每个重复工作流中都需重新构建底层逻辑，增加推理开销和失败率。本文提出，代理可通过将原子动作合成可复用的标准化操作流程（SOP），实现自我演化，这些SOP作为可调用的高层次工具，封装多步逻辑。我们进一步提出EvoSOP框架，使代理能从执行轨迹中提取SOP，并通过构建、合并、评估、修剪的系统化生命周期迭代优化工具集。大量实验表明，EvoSOP显著提升了任务成功率，同时大幅减少了交互轮次。分析还揭示，迭代工具优化促进了可靠高效的工具使用模式，为自演化代理的可扩展发展提供了路径。
原文摘要 · Abstract (English)
Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which forces agents to reinvent low-level logic for every recurring workflow, leading to increased reasoning overhead and failure rates. In this study, we propose that agents can achieve self-evolution by synthesizing these atomic actions into reusable Standard Operating Procedures (SOPs), which function as callable higher-order tools that encapsulate multi-step logic. We further introduce EvoSOP, a framework that empowers agents to extract SOPs from execution trajectories and iteratively optimize the toolset through a systematic lifecycle of construction, merging, evaluation, and pruning. Extensive experiments demonstrate that EvoSOP significantly boosts task success rates while substantially reducing the number of interaction rounds compared to baselines. Our analysis also reveals that iterative tool optimization fosters reliable and efficient tool-use patterns, providing a scalable pathway for the development of self-evolving agents.
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