A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm
  Bridging Foundation Models and Lifelong Agentic Systems
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.
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Jinyuan Fang
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2025-08-14T13:39:21.955Z
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Jinyuan Fang
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Yanwen Peng
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Xi Zhang
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2025-08-13T07:18:16.130Z
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Xi Zhang
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X-iZhang
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Yingxu Wang
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2025-08-13T07:18:10.017Z
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Yingxu Wang
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YingxuWang
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Xinhao Yi
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Guibin Zhang
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Yi Xu
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Bin Wu
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Siwei Liu
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Zihao Li
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2025-08-18T06:59:18.380Z
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Zihao Li
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ZihaoGla
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Zhaochun Ren
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Nikos Aletras
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Xi Wang
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2025-08-14T13:39:20.089Z
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Xi Wang
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XiWangEric
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Han Zhou
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Zaiqiao Meng
claimed_verified
2025-08-14T13:39:18.322Z
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Zaiqiao Meng
user
mengzaiqiao
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm
  Bridging Foundation Models and Lifelong Agentic Systems
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.
A survey of self-evolving AI agents that adapt to dynamic environments through automatic enhancement based on interaction data and feedback.
agent evolution self-evolving agentic systems feedback loop System Inputs Agent System Environment Optimisers domain-specific evolution strategies evaluation safety ethical considerations