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.

翻译：大语言模型（LLMs）催生了一类新型的智能体人工智能系统，它们能够通过调用外部工具进行推理、规划与执行。然而，现有的大多数智能体架构仍是集中式且单一化的，这限制了其可扩展性、专业化与互操作性。本文提出了一种可扩展的智能体智能框架，称为“智能体人工智能互联网”，其中自主、异构的智能体分布在云与边缘基础设施上，动态形成联盟以执行任务驱动的工作流。我们形式化了一种网络原生的智能体协作模型，并引入了一个激励相容的工作流-联盟可行性框架，该框架整合了能力覆盖、网络局部性与经济可实施性。为实现可扩展的协调，我们形式化了一个最小努力联盟选择问题，并提出了一种去中心化的联盟形成算法。所提出的框架可作为模型上下文协议（MCP）之上的一个协调层运行。一项医疗保健案例研究展示了领域专业化、云-边缘异构性以及动态联盟形成如何实现可扩展、有弹性且经济可行的智能体工作流。本研究为新兴的智能体人工智能互联网时代奠定了原则性协调与可扩展性的基础。