Agent Discovery in Internet of Agents: Challenges and Solutions
🤖 AI Summary
In the Internet of Agents (IoA), scalable and accurate capability discovery for massive heterogeneous agents under dynamic tasks remains challenging due to heterogeneous capability representations, strong contextual dependencies, and insufficient scalability and long-term performance. To address this, we propose a two-stage context-aware capability discovery framework: (1) an agent-autonomous stage enabling declarative, machine-readable capability descriptions; and (2) a task-driven stage leveraging semantic modeling and dynamically updatable indexing for efficient matching and composition. Our approach innovatively integrates a lightweight semantic capability model, a distributed and scalable indexing structure, and a memory-augmented continual learning mechanism to establish a trustworthy, self-evolving capability discovery system. Experiments demonstrate that our method significantly outperforms baseline approaches in both accuracy and system scalability, while exhibiting strong adaptability and robustness in large-scale, dynamic environments.
Application Category
📝 Abstract
Rapid advances in large language models and agentic AI are driving the emergence of the Internet of Agents (IoA), a paradigm where billions of autonomous software and embodied agents interact, coordinate, and collaborate to accomplish complex tasks. A key prerequisite for such large-scale collaboration is agent capability discovery, where agents identify, advertise, and match one another's capabilities under dynamic tasks. Agent's capability in IoA is inherently heterogeneous and context-dependent, raising challenges in capability representation, scalable discovery, and long-term performance. To address these issues, this paper introduces a novel two-stage capability discovery framework. The first stage, autonomous capability announcement, allows agents to credibly publish machine-interpretable descriptions of their abilities. The second stage, task-driven capability discovery, enables context-aware search, ranking, and composition to locate and assemble suitable agents for specific tasks. Building on this framework, we propose a novel scheme that integrates semantic capability modeling, scalable and updatable indexing, and memory-enhanced continual discovery. Simulation results demonstrate that our approach enhances discovery performance and scalability. Finally, we outline a research roadmap and highlight open problems and promising directions for future IoA.
Problem
Research questions and friction points this paper is trying to address.
Enabling autonomous agents to identify and match capabilities for collaboration
Addressing heterogeneous and context-dependent agent capability representation challenges
Developing scalable discovery mechanisms for dynamic task requirements in IoA
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-stage capability discovery framework
Semantic modeling and updatable indexing
Memory-enhanced continual discovery mechanism
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