Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence
This work addresses the problem of fragmented theoretical understanding in transitioning LLMs to autonomous systems for AI researchers, though it appears incremental as a conceptual review.
The paper tackles the gap between token-level processing and system-level intelligence in LLMs by proposing AgentOS, a conceptual framework that redefines LLMs as a 'Reasoning Kernel' with structured operating system logic, aiming to architect resilient and scalable cognitive environments.
The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While current research primarily focuses on scaling context windows or optimizing prompt engineering the theoretical bridge between micro scale token processing and macro scale systemic intelligence remains fragmented.This paper proposes AgentOS,a holistic conceptual framework that redefines the LLM as a "Reasoning Kernel" governed by structured operating system logic.Central to this architecture is Deep Context Management which conceptualizes the context window as an Addressable Semantic Space rather than a passive buffer.We systematically deconstruct the transition from discrete sequences to coherent cognitive states introducing mechanisms for Semantic Slicing and Temporal Alignment to mitigate cognitive drift in multi-agent orchestration.By mapping 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 cognitive environments.Our analysis asserts that the next frontier of AGI development lies in the architectural efficiency of system-level coordination.