# Agent Discovery in Internet of Agents: Challenges and Solutions

arXiv: https://arxiv.org/abs/2511.19113 · HTML: https://mlsanity.com/paper/2511.19113/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2025.

## Built on by (most cited first, 6 of 6)

- [Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP](https://mlsanity.com/paper/2602.11327/) (2026)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration](https://mlsanity.com/paper/2606.30246/) (2026)
- [InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents](https://mlsanity.com/paper/2608.13030/) (2026)
- [A Scalable Trust Discovery Architecture for the Internet of Agents](https://mlsanity.com/paper/2609.20095/) (2026)
- [SemDHT: Certified Semantic Discovery for Peer-to-Peer Agent Networks over Exact-Key DHTs](https://mlsanity.com/paper/2609.23539/) (2026)

## Abstract (arXiv)

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.