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

TL;DR

This paper introduces AgentOS, a comprehensive framework that transforms LLMs into dynamic, system-like cognitive agents by integrating OS principles and semantic context management to enable emergent intelligence.

Contribution

It presents a novel architecture that bridges token-level processing with system-level intelligence using OS-inspired abstractions and semantic management techniques.

Findings

Proposes Deep Context Management as an addressable semantic space

Introduces mechanisms for Semantic Slicing and Temporal Alignment

Maps OS abstractions onto LLM constructs for scalable cognition

Abstract

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.