Designing a Distributed LLM-Based Search Engine as a Foundation for Agent Discovery

This paper explores the potential of decentralized AI-powered Search Engine agents for managing tasks across vari-ous domains. By simulating real-world scenarios, we demonstrate how these agents collaborate within a distributed framework to perform complex tasks efficiently. Central to this system is an AI-powered Search Engine that facilitates seamless matchmaking between agents, allowing them to share expertise and resources. This search engine leverages the power and capabilities of large language models (LLMs), enhancing agent discovery and interaction. The study highlights the multi-level functionality of agents, ranging from basic API interactions to advanced decision-making capabilities. The communication protocols implemented enable smooth agent collaboration and task completion. This work sets the foundation for future advancements in decentralized agent networks, contributing to the development of more sophisticated, equitable, and secure digital ecosystems.