Comprehensive Survey of LLM-based Agent Operating Systems

Large language model-based agent operating systems(Agent OS),as core platforms for integrating large models,tool resources,and multi-agent collaboration,are gradually becoming a key research direction for advancing general artificial intelligence.This paper systematically reviews the research progress in the field of Agent OS.It begins by discussing foundational theories,reviewing the evolution of various large language models,and progress in agent technology and traditional operating systems.This paper then elaborates on how their hierarchical architectures and modular designs achieve resource management and intelligent scheduling,focusing on typical architectures such as AIOS.Furthermore,it clarifies existing technical bottlenecks in scalability,context integration,and security within current systems.It also proposes future directions,including the use of lightweight designs,self-supervised learning mechanisms,and dynamic scheduling algorithms to optimize multi-agent cooperation efficiency.The main contributions of this paper are integrating fragmented research to provide a clearer technical framework,and highlighting the current limitations of Agent OS in covering emerging applications and industry-specific customizations.Future work should focus on enhancing the capability of cross-domain Agent OS for self-evolution and accelerating their implementation across diverse fields.