title: Large Language Model Agent: A Survey on Methodology, Applications and
    Challenges
summary: The era of intelligent agents is upon us, driven by revolutionary
  advancements in large language models. Large Language Model (LLM) agents, with
  goal-driven behaviors and dynamic adaptation capabilities, potentially
  represent a critical pathway toward artificial general intelligence. This
  survey systematically deconstructs LLM agent systems through a
  methodology-centered taxonomy, linking architectural foundations, collaboration
  mechanisms, and evolutionary pathways. We unify fragmented research threads by
  revealing fundamental connections between agent design principles and their
  emergent behaviors in complex environments. Our work provides a unified
  architectural perspective, examining how agents are constructed, how they
  collaborate, and how they evolve over time, while also addressing evaluation
  methodologies, tool applications, practical challenges, and diverse application
  domains. By surveying the latest developments in this rapidly evolving field,
  we offer researchers a structured taxonomy for understanding LLM agents and
  identify promising directions for future research. The collection is available
  at https://github.com/luo-junyu/Awesome-Agent-Papers.
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paper_authors.user: joeyleo
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paper_authors.name: Qingqing Long
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paper_authors.name: Rongcheng Tu
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paper_authors.name: Xiao Luo
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paper_authors.fullname: Xiao Luo
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paper_authors.name: Wei Ju
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paper_authors.name: Hanqing Zhao
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paper_authors.name: Dacheng Tao
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paper_authors.name: Ming Zhang
paper_title: Large Language Model Agent: A Survey on Methodology, Applications and
    Challenges
paper_summary: The era of intelligent agents is upon us, driven by revolutionary
  advancements in large language models. Large Language Model (LLM) agents, with
  goal-driven behaviors and dynamic adaptation capabilities, potentially
  represent a critical pathway toward artificial general intelligence. This
  survey systematically deconstructs LLM agent systems through a
  methodology-centered taxonomy, linking architectural foundations, collaboration
  mechanisms, and evolutionary pathways. We unify fragmented research threads by
  revealing fundamental connections between agent design principles and their
  emergent behaviors in complex environments. Our work provides a unified
  architectural perspective, examining how agents are constructed, how they
  collaborate, and how they evolve over time, while also addressing evaluation
  methodologies, tool applications, practical challenges, and diverse application
  domains. By surveying the latest developments in this rapidly evolving field,
  we offer researchers a structured taxonomy for understanding LLM agents and
  identify promising directions for future research. The collection is available
  at https://github.com/luo-junyu/Awesome-Agent-Papers.
paper_ai_summary: A survey of large language model agents provides a structured taxonomy, exploring architectural principles, collaboration, evolution, evaluation, and applications, highlighting promising research directions.
paper_ai_keywords: Large Language Model (LLM) goal-driven behaviors dynamic adaptation capabilities architectural foundations collaboration mechanisms evolutionary pathways agent design principles emergent behaviors evaluation methodologies tool applications practical challenges application domains