Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects
TL;DR
An organizing framework for understanding LLM‐based agents is established, systematically deconstructing both single‐agent and multi‐agent systems into their core components, and the architectural principles and key mechanisms that underpin their intelligence are analyzed.
Abstract
The concept of the intelligent agent represents a long‐standing pursuit in artificial intelligence. Recent breakthroughs in large language models (LLMs) have catalyzed a paradigm shift, enabling the development of sophisticated agents that exhibit advanced reasoning, planning, and tool‐use capabilities across diverse domains. These LLM‐based agents, which leverage natural language as a universal interface for cognition and interaction, are rapidly advancing from theoretical constructs to practical applications, ranging from autonomous task assistants to complex multi‐agent simulations of social and economic systems. This paper provides an integrative survey of this burgeoning field. We first establish an organizing framework for understanding LLM‐based agents, systematically deconstructing both single‐agent and multi‐agent systems into their core components. We analyze the architectural principles and key mechanisms that underpin their intelligence, including planning paradigms, memory structures, and reflection‐based self‐improvement. We further investigate the dynamics of multi‐agent systems, exploring coordination strategies, communication protocols, and organizational structures. The paper also covers the crucial aspects of performance evaluation, highlighting influential benchmarks and identifying key challenges. Finally, we synthesize the current landscape to discuss the primary challenges, such as the intrinsic limitations of LLMs and the complexities of ensuring safety and alignment, and chart a course for future research directions, including the drive toward continual learning and enhanced multi‐modal capabilities.