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

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

Aug 10, 2025Β·
Jinyuan Fang
,
Yanwen Peng
Xi Zhang
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
Β· 2 min read
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 component include 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.
Type
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This survey provides a comprehensive overview of the latest advances in the field of self-evolving AI agents, highlighting key technological shifts and development trends.

Paradigm Definition

We define the new paradigm of “Self-Evolving AI Agents” as bridging foundation models and lifelong agentic systems.

Concept Definition of Self-Evolving AI Agents

Self-evolving AI agents are autonomous systems that continuously and systematically optimise their internal components through interaction with environments, with the goal of adapting to changing tasks, contexts and resources while preserving safety and enhancing performance.

Guiding Principles: Three Laws

Inspired by Asimov’s Three Laws of Robotics, we propose the Three Laws of Self-Evolving AI Agents:

  • Endure (Safety Adaptation): Any modification must maintain safety and stability.
  • Excel (Performance Preservation): Subject to the first law, agents must preserve or enhance task performance.
  • Evolve (Autonomous Evolution): Subject to the first and second laws, agents must autonomously optimize their internal components in response to changes.

Unified Framework and Technical Review

We propose a unified conceptual framework with four core components: System Inputs, Agent System, Environment, and Optimisers. The survey systematically reviews evolution strategies across foundation models, prompts, memory, tools, workflows, and inter-agent communication.

Single-Agent Optimisation

We explore optimisation techniques for individual self-evolving agents, focusing on methods that enhance their learning and adaptation capabilities.

Multi-Agent Optimisation

We investigate strategies for optimising the performance of multiple self-evolving agents working collaboratively, addressing challenges such as communication, coordination, and resource sharing.

Domain Adaptation and Challenges

We also analyze domain-specific evolution methods in biomedicine, programming, and finance, and discuss key challenges in evaluation, safety, and ethics, laying the foundation for the next generation of adaptive, autonomous, lifelong agentic systems.

BibTeX

@misc{fang2025comprehensivesurveyselfevolvingai,
      title={A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems}, 
      author={Jinyuan Fang and Yanwen Peng and Xi Zhang and Yingxu Wang and Xinhao Yi and Guibin Zhang and Yi Xu and Bin Wu and Siwei Liu and Zihao Li and Zhaochun Ren and Nikos Aletras and Xi Wang and Han Zhou and Zaiqiao Meng},
      year={2025},
      eprint={2508.07407},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2508.07407}, 
}
Survey AI Agents Self-Evolving
Xi Zhang
Authors
Xi Zhang
CS PhD Candidate @ AI4BioMed Lab
Interested in AI4Health & AI Agent, specialising in the development of steerable radiology vision-language systems.

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