A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
🤖 AI Summary
Existing AI agents are predominantly statically configured, limiting their adaptability to dynamic environments and thereby constraining their capability to solve complex real-world tasks.
Method: This paper presents a systematic survey of state-of-the-art self-evolving AI agents and introduces the first unified conceptual framework—comprising system input, agent, environment, and optimizer—and establishes a general feedback-loop mechanism. It classifies and integrates evolutionary strategies for foundation models and continual learning, leveraging large language models (LLMs) to jointly automate structural and behavioral evolution through interaction data and environmental feedback.
Contribution/Results: The framework enables domain-specific evolutionary pathways across biomedicine, programming, and finance. Empirical results demonstrate substantial improvements in environmental adaptability and task reliability, advancing the frontier of autonomous, lifelong-learning AI agents.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
AI agents lack adaptability in dynamic environments
Need for automatic agent evolution techniques
Survey reviews self-evolving agent systems and frameworks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-evolving AI agents bridge foundation models and lifelong systems
Agent evolution techniques enhance systems via interaction data
Unified framework includes inputs, agents, environment, optimisers
🔎 Similar Papers
No similar papers found.
