Survey of Self-Evolving AI Agents: Bridging Foundation Models and Lifelong Adaptability

Recent advances in large language models have sparked growing interest in AI agents capable of solving complex, real-world tasks.

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.

The framework highlights four key components: System Inputs, Agent System, Environment, and Optimisers, serving as a foundation for understanding and comparing different strategies.

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.