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

2026-02-03

AI Summary

This work proposes the first network-native, decentralized collaboration framework for the Internet of Agents, addressing the limited scalability of existing agent systems that rely on centralized architectures in large-scale, heterogeneous environments. By integrating capability coverage, network locality, and economic incentives, the framework enables task-driven dynamic team formation through coalition feasibility analysis, modeling of minimal-effort coalition selection, and a decentralized formation algorithm. Designed to be compatible with the Model Context Protocol (MCP), the approach is validated in a healthcare setting, demonstrating significant improvements in domain specialization, cloud-edge coordination, and dynamic workflow orchestration. The results highlight enhanced scalability, robustness, and economic efficiency compared to conventional architectures.

Technology Category

• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Agentic AI
• Scalability
• Interoperability
• Distributed Teaming
• Workflow Coordination

Innovation

• Internet of Agentic AI
• incentive-compatible coalition formation
• decentralized coordination
• heterogeneous agentic workflows
• cloud-edge intelligence