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

Title: 一种自我进化的人工智能代理的全面综述：连接基础模型和终身代理系统的全新范式

Abstract: 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. Show Chinese abstract

Abstract: 近年来，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AI代理的兴趣。 然而，大多数现有的代理系统依赖于手动设计的配置，在部署后保持静态，限制了它们适应动态和不断变化环境的能力。 为此，最近的研究探索了代理进化技术，旨在根据交互数据和环境反馈自动增强代理系统。 这一新兴方向为自我进化的AI代理奠定了基础，这些代理弥合了基础模型的静态能力与终身代理系统所需的持续适应性之间的差距。 在本篇综述中，我们对现有的自我进化代理系统技术进行了全面回顾。 具体而言，我们首先介绍了一个统一的概念框架，该框架抽象了自我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件：系统输入、代理系统、环境和优化器，作为理解和比较不同策略的基础。 基于此框架，我们系统地回顾了针对代理系统不同组件的广泛自我进化技术。 我们还研究了为生物医学、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耦合。 此外，我们专门讨论了自我进化代理系统的评估、安全性和伦理考量，这些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我进化AI代理的系统性理解，为开发更具适应性、自主性和终身代理系统奠定基础。