让AI Agent像生物一样进化，自动学习技能并协作完成复杂任务。
EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation
- 将技能封装为可触发的结构化模块，支持持续优化
- 在外贸场景中使GPT5.2评分提升约28%
- 适合需要长期能力积累与多智能体协作的系统
本文提出EvoAgent——一个集成结构化技能学习与分层子代理委派机制的可演化大语言模型（LLM）智能体框架。该框架将技能建模为带触发机制和演化元数据的多文件结构化能力单元，并通过用户反馈驱动的闭环过程实现技能的持续生成与优化。结合三阶段技能匹配策略与三层记忆架构，支持复杂问题的动态分解与长期能力积累。基于真实外贸场景的实验表明，集成EvoAgent后，GPT5.2在专业性、准确性和实用性方面均有显著提升。在五维LLM-as-Judge评估体系下，平均得分提升约28%。进一步的模型迁移实验显示，智能体系统性能不仅取决于底层模型的内在能力，更取决于模型与智能体架构之间的协同程度。代码、数据与文档将开源至https://github.com/Focus-AI-Center/Mentarc-EvoAgent.git。
原文摘要 · Abstract (English)
This paper proposes EvoAgent--an evolvable large language model (LLM) agent framework that integrates structured skill learning with a hierarchical sub-agent delegation mechanism. EvoAgent models skills as multi-file structured capability units equipped with triggering mechanisms and evolutionary metadata, and enables continuous skill generation and optimization through a user-feedback-driven closed-loop process. In addition, by incorporating a three-stage skill matching strategy and a three-layer memory architecture, the framework supports dynamic task decomposition for complex problems and long-term capability accumulation. Experimental results based on real-world foreign trade scenarios demonstrate that, after integrating EvoAgent, GPT5.2 achieves significant improvements in professionalism, accuracy, and practical utility. Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28\%. Further model transfer experiments indicate that the performance of an agent system depends not only on the intrinsic capabilities of the underlying model, but also on the degree of synergy between the model and the agent architecture. Code, data, and documents will be released at https://github.com/Focus-AI-Center/Mentarc-EvoAgent.git.
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