自进化AI智能体综合研究：连接基础模型与终身代理系统的新范式

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

August 10, 2025

作者: Jinyuan Fang, Yanwen Peng, Xi Zhang, Yingxu Wang, Xinhao Yi, Guibin Zhang, Yi Xu, Bin Wu, Siwei Liu, Zihao Li, Zhaochun Ren, Nikos Aletras, Xi Wang, Han Zhou, Zaiqiao Meng

cs.AI

摘要

近期大型语言模型的进展引发了人们对能够解决复杂现实任务的人工智能代理日益增长的兴趣。然而，现有的大多数代理系统依赖于手动配置，这些配置在部署后保持静态，限制了其适应动态和不断变化环境的能力。为此，最新研究探索了旨在基于交互数据和环境反馈自动增强代理系统的进化技术。这一新兴方向为自进化AI代理奠定了基础，将基础模型的静态能力与终身代理系统所需的持续适应性相连接。在本综述中，我们对现有的自进化代理系统技术进行了全面回顾。具体而言，我们首先引入了一个统一的概念框架，抽象出自进化代理系统设计背后的反馈循环。该框架突出了四个关键组成部分：系统输入、代理系统、环境和优化器，为理解和比较不同策略提供了基础。基于此框架，我们系统地回顾了针对代理系统不同组件的多种自进化技术。我们还探讨了为生物医学、编程和金融等专业领域开发的特定领域进化策略，其中优化目标与领域约束紧密相关。此外，我们专门讨论了自进化代理系统的评估、安全性和伦理考量，这对于确保其有效性和可靠性至关重要。本综述旨在为研究人员和实践者提供对自进化AI代理的系统性理解，为开发更具适应性、自主性和终身性的代理系统奠定基础。

English

Recent advances in large language models have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manually crafted configurations that remain static after deployment, limiting their ability to adapt to dynamic and evolving environments. To this end, recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging direction lays the foundation for self-evolving AI agents, which bridge the static capabilities of foundation models with the continuous adaptability required by lifelong agentic systems. In this survey, we provide a comprehensive review of existing techniques for self-evolving agentic systems. Specifically, we first introduce a unified conceptual framework that abstracts the feedback loop underlying the design of self-evolving agentic systems. The framework highlights four key components: System Inputs, Agent System, Environment, and Optimisers, serving as a foundation for understanding and comparing different strategies. Based on this framework, we systematically review a wide range of self-evolving techniques that target different components of the agent system. We also investigate domain-specific evolution strategies developed for specialised fields such as biomedicine, programming, and finance, where optimisation objectives are tightly coupled with domain constraints. In addition, we provide a dedicated discussion on the evaluation, safety, and ethical considerations for self-evolving agentic systems, which are critical to ensuring their effectiveness and reliability. This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents, laying the foundation for the development of more adaptive, autonomous, and lifelong agentic systems.