标题： FlexNGIA 2.0：通过代理人工智能重新设计互联网 - 由人工智能设计、部署和管理的协议、服务和流量工程 显示英文标题

标题： FlexNGIA 2.0: Redesigning the Internet with Agentic AI - Protocols, Services, and Traffic Engineering Designed, Deployed, and Managed by AI

摘要： 随着沉浸式通信的需求不断增长，以及网络软件化和AI驱动的认知与生成推理的进步，为重新思考和重塑未来互联网创造了关键机遇。 在这一背景下，本文介绍了FlexNGIA 2.0，这是一种基于代理的AI驱动的互联网架构，利用基于大语言模型（LLM）的AI代理自主协调、配置和演进网络。 这些代理可以在运行时感知、推理，并在彼此之间协调，以动态设计、实施、部署和适应通信协议、服务功能链（SFCs）、网络功能、资源分配策略、拥塞控制和流量工程方案，从而确保在不断变化的条件下实现最佳性能、可靠性和效率。 本文首先概述了FlexNGIA 2.0的整体架构及其基于LLM的AI代理。 对于每个代理，我们详细描述了其设计、实现、输入和输出、提示结构、与工具和其他代理的交互，并进行了初步的概念验证实验，展示了其操作和潜力。 结果明确突显了这些基于LLM的AI代理在自动化设计、实施、部署和性能评估传输协议、服务功能链、网络功能、拥塞控制方案和资源分配策略方面的能力。 FlexNGIA 2.0为新一代基于代理的AI驱动网络铺平了道路，在这种网络中，完全认知的、自我演化的AI代理可以自主设计、实施、适应和优化网络的协议、算法和行为，以在复杂、动态和异构环境中高效运行。 为了将这一愿景变为现实，我们还识别了实现完全自主、自适应和基于代理的AI驱动网络的关键研究挑战。 显示英文摘要

摘要： The escalating demands of immersive communications, alongside advances in network softwarization and AI-driven cognition and generative reasoning, create a pivotal opportunity to rethink and reshape the future Internet. In this context, we introduce in this paper, FlexNGIA 2.0, an Agentic AI-driven Internet architecture that leverages LLM-based AI agents to autonomously orchestrate, configure, and evolve the network. These agents can, at runtime, perceive, reason, coordinate among themselves to dynamically design, implement, deploy, and adapt communication protocols, Service Function Chains (SFCs), network functions, resource allocation strategies, congestion control, and traffic engineering schemes, thereby ensuring optimal performance, reliability, and efficiency under evolving conditions. The paper first outlines the overall architecture of FlexNGIA 2.0 and its constituent LLM-Based AI agents. For each agent, we detail its design, implementation, inputs and outputs, prompt structures, interactions with tools and other agents, followed by preliminary proof-of-concept experiments demonstrating its operation and potential. The results clearly highlight the ability of these LLM-based AI agents to automate the design, the implementation, the deployment, and the performance evaluation of transport protocols, service function chains, network functions, congestion control schemes, and resource allocation strategies. FlexNGIA 2.0 paves the way for a new class of Agentic AI-Driven networks, where fully cognitive, self-evolving AI agents can autonomously design, implement, adapt and optimize the network's protocols, algorithms, and behaviors to efficiently operate across complex, dynamic, and heterogeneous environments. To bring this vision to reality, we also identify key research challenges toward achieving fully autonomous, adaptive, and agentic AI-driven networks.