Fetch.ai: An Architecture for Modern Multi-Agent Systems
- URL: http://arxiv.org/abs/2510.18699v1
- Date: Tue, 21 Oct 2025 14:53:56 GMT
- Title: Fetch.ai: An Architecture for Modern Multi-Agent Systems
- Authors: Michael J. Wooldridge, Attila Bagoly, Jonathan J. Ward, Emanuele La Malfa, Gabriel Paludo Licks,
- Abstract summary: Recent surges in LLM-driven intelligent systems largely overlook decades of foundational multi-agent systems (MAS) research.<n>Fetch.ai is an industrial-strength platform designed to bridge this gap by facilitating the integration of classical MAS principles with modern AI capabilities.<n>We present a novel, multi-layered solution built on a decentralized foundation of on-chain blockchain services for verifiable identity, discovery, and transactions.
- Score: 10.4021644658645
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent surges in LLM-driven intelligent systems largely overlook decades of foundational multi-agent systems (MAS) research, resulting in frameworks with critical limitations such as centralization and inadequate trust and communication protocols. This paper introduces the Fetch.ai architecture, an industrial-strength platform designed to bridge this gap by facilitating the integration of classical MAS principles with modern AI capabilities. We present a novel, multi-layered solution built on a decentralized foundation of on-chain blockchain services for verifiable identity, discovery, and transactions. This is complemented by a comprehensive development framework for creating secure, interoperable agents, a cloud-based platform for deployment, and an intelligent orchestration layer where an agent-native LLM translates high-level human goals into complex, multi-agent workflows. We demonstrate the deployed nature of this system through a decentralized logistics use case where autonomous agents dynamically discover, negotiate, and transact with one another securely. Ultimately, the Fetch.ai stack provides a principled architecture for moving beyond current agent implementations towards open, collaborative, and economically sustainable multi-agent ecosystems.
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