Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow
This work addresses scalability, specialization, and interoperability issues in agentic AI systems, laying a foundation for principled coordination in distributed AI, though it appears incremental in building on existing agentic architectures.
The paper tackles the problem of centralized and monolithic agentic AI systems by proposing a framework for scalable agentic intelligence, called the Internet of Agentic AI, which enables autonomous, heterogeneous agents to dynamically form coalitions for task-driven workflows, as demonstrated in a healthcare case study.
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.