发表机构
Beijing University of Posts and Telecommunications; China Telecom Cloud Network Operating System R&D Center; China Telecom Research Institute; University of Science and Technology Beijing（北京邮电大学; 中国电信云网络操作系统研发中心; 中国电信研究院; 北京科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将LLM智能体集成至光网络生命周期管理，构建分层多智能体框架，实现从任务级半自动到生命周期级全自主的演进愿景。
AI 中文摘要
随着光网络在规模、复杂性和服务多样性方面持续扩展，自动化的实施对于确保光网络生命周期管理（LCM）的敏捷性、效率和可靠性变得至关重要。大语言模型（LLM）智能体，以其在逻辑推理、自适应决策、复杂问题求解和多任务编排方面日益成熟的能力而著称，为超越传统AI技术推进网络自动化提供了巨大机遇。然而，LLM智能体在光网络中的应用仍处于早期探索阶段，面临多任务协调缺失、高计算需求、数据依赖和可靠性问题等挑战。在本文中，我们设想了一条通过将LLM智能体以高度自主性集成到整个生命周期管理中，迈向智能体光网络（AONs）的概念性路线图。首先，我们追溯了从人工操作到AI赋能框架的演变过程，并提炼了智能体中的关键技术，为利用其优势解决实际网络自动化挑战提供了可操作的见解。本文的一个核心贡献是提出了一个分层多智能体框架，该框架专门开发用于管理AONs生命周期管理中的每个阶段，包括规划、部署、运营、维护、升级和退役，从而在整个生命周期中实现更具凝聚力和全面性的自动化。此外，本文还讨论了LLM与光网络交叉领域的未来方向和潜在挑战。通过将LLM智能体与AONs的特定需求对齐，这项工作旨在探索光网络从任务级半自动执行向生命周期级完全自主演进的潜力。
英文摘要
As optical networks continue to expand in scale, complexity, and service diversity, the implementation of automation has become essential for ensuring agility, efficiency, and reliability in lifecycle management (LCM) of optical networks. Large language model (LLM) Agent, distinguished by its progressively sophisticated capabilities in logical reasoning, adaptive decision-making, complex problem solving, and multi-task orchestration, presents great opportunities to advance network automation beyond traditional AI techniques. Nevertheless, the application of LLM Agent in optical networks remains in its early exploratory stage, challenged by the lack of multi-task coordination, high computational demands, data dependence, and reliability concerns. In this paper, we envision a conceptual roadmap toward Agentic Optical Networks (AONs) by integrating LLM Agents throughout the LCM with high-level autonomy. First, we trace the evolution from manual operations to AI-empowered frameworks and distill key technologies in Agent, providing actionable insights into leveraging its strengths for addressing practical network automation challenges. A core contribution of this paper is the proposal of a hierarchical multi-Agent framework, which is specifically developed to manage every phase in LCM of AONs, including planning, deployment, operation, maintenance, upgrade, and decommission, thereby enabling more cohesive and comprehensive automation throughout the entire lifecycle. In addition, future directions and underlying challenges are also discussed at the intersection of LLM and optical networks. By aligning the LLM Agent with the specialized requirements of AONs, this work aims to explore the potential for the evolution of optical networks moving from task-level semi-automatic execution toward lifecycle-level full autonomy.
