AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem

TL;DR

This paper introduces AgentOS, a novel natural language-driven data ecosystem that replaces traditional GUIs with a unified NUI, enabling autonomous agent coordination, modular skills, and continuous knowledge discovery for intelligent computing.

Contribution

It proposes the concept of AgentOS, a paradigm shift from application silos to a unified, natural language-driven operating system centered on an Agent Kernel and modular skills.

Findings

AgentOS enables seamless natural language interaction with computing environments.

The system integrates real-time intent mining and knowledge discovery.

It transforms OS architecture into a continuous data mining pipeline.

Abstract

The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as OpenClaw demonstrate that Large Language Model (LLM)-based agents can autonomously operate local computing environments, orchestrate workflows, and integrate external tools. However, within the current paradigm, these agents remain conventional applications running on legacy operating systems originally designed for Graphical User Interfaces (GUIs) or Command Line Interfaces (CLIs). This architectural mismatch leads to fragmented interaction models, poorly structured permission management (often described as "Shadow AI"), and severe context fragmentation. This paper proposes a new paradigm: a Personal Agent Operating System (AgentOS). In AgentOS, traditional GUI desktops are replaced by a Natural User Interface (NUI) centered on a unified natural language or voice portal. The system core becomes an Agent Kernel that interprets user intent, decomposes tasks, and coordinates multiple agents, while traditional applications evolve into modular Skills-as-Modules enabling users to compose software through natural language rules. We argue that realizing AgentOS fundamentally becomes a Knowledge Discovery and Data Mining (KDD) problem. The Agent Kernel must operate as a real-time engine for intent mining and knowledge discovery. Viewed through this lens, the operating system becomes a continuous data mining pipeline involving sequential pattern mining for workflow automation, recommender systems for skill retrieval, and dynamically evolving personal knowledge graphs. These challenges define a new research agenda for the KDD community in building the next generation of intelligent computing systems.