Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow

TL;DR

This paper introduces the Internet of Agentic AI, a scalable framework where heterogeneous autonomous agents form coalitions to execute workflows, enhancing scalability, specialization, and interoperability in agentic systems.

Contribution

It formalizes a network-native model for agent collaboration, proposes a decentralized coalition formation algorithm, and demonstrates its effectiveness through a healthcare case study.

Findings

Enables dynamic coalition formation for scalable workflows

Improves resilience and economic viability of agentic AI systems

Provides a formal framework for agent collaboration and coordination

Abstract

Large language models (LLMs) have enabled a new class of agentic AI systems that reason, plan, and act by invoking external tools. However, most existing agentic architectures remain centralized and monolithic, limiting scalability, specialization, and interoperability. This paper proposes a framework for scalable agentic intelligence, termed the Internet of Agentic AI, in which autonomous, heterogeneous agents distributed across cloud and edge infrastructure dynamically form coalitions to execute task-driven workflows. We formalize a network-native model of agentic collaboration and introduce an incentive-compatible workflow-coalition feasibility framework that integrates capability coverage, network locality, and economic implementability. To enable scalable coordination, we formulate a minimum-effort coalition selection problem and propose a decentralized coalition formation algorithm. The proposed framework can operate as a coordination layer above the Model Context Protocol (MCP). A healthcare case study demonstrates how domain specialization, cloud-edge heterogeneity, and dynamic coalition formation enable scalable, resilient, and economically viable agentic workflows. This work lays the foundation for principled coordination and scalability in the emerging era of Internet of Agentic AI.