Internet of Agents: Fundamentals, Applications, and Challenges
🤖 AI Summary
AI agents exhibit high heterogeneity, weak interoperability, and lack a unified infrastructure for large-scale coordination. Method: This paper proposes the Internet of Agents (IoA) framework—the first agent-centric infrastructure enabling large-scale interconnection and collaborative orchestration. It systematically defines the IoA concept and introduces a layered architecture; innovatively establishes five enabling paradigms: capability advertisement and discovery, adaptive communication, dynamic task matching, consensus-based conflict resolution, and game-theoretic incentive mechanisms. Key technologies integrated include multimodal large-model reasoning, semantic service discovery, distributed negotiation protocols, and lightweight communication middleware. Contribution/Results: The work establishes the first comprehensive IoA theoretical framework and technology roadmap, identifies six fundamental open challenges, and lays the foundational groundwork for building trustworthy, resilient, and scalable agent ecosystems.
Application Category
📝 Abstract
With the rapid proliferation of large language models and vision-language models, AI agents have evolved from isolated, task-specific systems into autonomous, interactive entities capable of perceiving, reasoning, and acting without human intervention. As these agents proliferate across virtual and physical environments, from virtual assistants to embodied robots, the need for a unified, agent-centric infrastructure becomes paramount. In this survey, we introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale. We begin by presenting a general IoA architecture, highlighting its hierarchical organization, distinguishing features relative to the traditional Internet, and emerging applications. Next, we analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models. Finally, we identify open research directions toward building resilient and trustworthy IoA ecosystems.
Problem
Research questions and friction points this paper is trying to address.
Establishing a unified infrastructure for interconnected AI agents
Enabling seamless collaboration among heterogeneous autonomous agents
Addressing operational challenges in large-scale agent ecosystems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical organization for agent interconnection
Dynamic discovery and collaborative orchestration
Adaptive communication and task matching protocols
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