Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence

The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitivethis http URLcurrent research primarily focuses on scaling context windows or optimizing prompt engineering the theoretical bridge between micro scale token processing and macro scale systemic intelligence remainsthis http URLpaper proposes AgentOS,a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating systemthis http URLto this architecture is Deep Context Management which conceptualizes the context window as an Addressable Semantic Space rather than a passivethis http URLsystematically deconstruct the transition from discrete sequences to coherent cognitive states introducing mechanisms for Semantic Slicing and Temporal Alignment to mitigate cognitive drift in multi-agentthis http URLmapping classical OS abstractions such as memory paging interrupt handling and process scheduling onto LLM native constructs, this review provides a rigorous roadmap for architecting resilient scalable and self-evolving cognitivethis http URLanalysis asserts that the next frontier of AGI development lies in the architectural efficiency of system-level coordination.