MOD-X: A Modular Open Decentralized eXchange Framework proposal for Heterogeneous Interoperable Artificial Intelligence Agents

TL;DR

MOD-X is a new layered framework designed to enable secure, decentralized, and interoperable communication among heterogeneous AI agents, facilitating scalable ecosystems with dynamic workflows and semantic discovery.

Contribution

It introduces a modular, layered architecture with novel features like a Universal Message Bus, semantic discovery, and blockchain security for agent interoperability.

Findings

Demonstrates integration of diverse AI agents using MOD-X

Highlights improved scalability and security in agent ecosystems

Provides a practical example of heterogeneous agent communication

Abstract

As Artificial Intelligence systems evolve from monolithic models to ecosystems of specialized agents, the need for standardized communication protocols becomes increasingly critical. This paper introduces MOD-X (Modular Open Decentralized eXchange), a novel architectural framework proposal for agent interoperability that addresses key limitations of existing protocols. Unlike current approaches, MOD-X proposes a layered architecture with a Universal Message Bus, thorough state management, translation capabilities, and blockchain-based security mechanisms. We present MOD-X's architecture, compare it with existing protocols, and demonstrate its application through a worked example how it enables integration between heterogeneous specialist agents (agents with different architectures, vendors, capabilities, and knowledge representations--including rule-based systems, neural networks, symbolic reasoning engines, and legacy software with agent wrappers). MOD-X's key innovations include a publish-subscribe communication model, semantic capability discovery, and dynamic workflow orchestration--providing a framework that bridges theoretical formalism with practical implementation. This architecture addresses the growing need for truly decentralized, interoperable agent ecosystems that can scale effectively without the need for central coordination.