Summary
Managing optical networks, which handle large amounts of internet traffic, is getting more complex and needs better automation to keep everything running smoothly. The authors describe how large language models (LLM agents), which can think logically and solve problems, could be used to automate all stages of network management, from setup to maintenance and upgrade. They propose a system where multiple LLM agents work together in a hierarchy, each handling different parts of the network's lifecycle to improve efficiency and reliability. This is still an early idea and faces challenges like high computing needs and data requirements, but the authors outline a roadmap for future development.
What this means in practice
- •For network operators: Coordinate and automate planning, deployment, and maintenance phases of optical networks to improve operational efficiency and reliability.
- •For it infrastructure teams: Use multi-agent LLM frameworks to manage complex lifecycle events across large-scale network infrastructures more cohesively.
A position paper. It proposes an approach and reports no results.
Authors
Yao Zhang, Shengnan Li, Yuchen Song, Yidi Wang, Yue Pang, Wenbin Chen, Xiaotian Jiang, Xiao Luo, Meixia Fu, Min Zhang, Yongli Zhao, Shanguo Huang, Alan Pak Tao Lau, Danshi Wang
Abstract
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.