Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management

2026-09-26

AI Summary

This study addresses the challenges of inadequate multi-task coordination, high computational dependency, and insufficient reliability in optical network automation by proposing the vision of Agent-based Optical Networks (AON), which leverages large language model (LLM)-driven autonomous management across the entire network lifecycle. Methodologically, a hierarchical multi-agent architecture is constructed to enable closed-loop control spanning all phases from network planning to decommissioning, thereby facilitating the evolution of optical networks from semi-automated operation toward full autonomy. Furthermore, this work establishes a conceptual roadmap for AON, offering actionable theoretical insights and forward-looking technical directions to overcome existing bottlenecks in network automation.

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.

Problem

• Optical Networks
• Large Language Model Agent
• Lifecycle Management
• Network Automation
• Agentic Optical Networks

Innovation

• Large Language Model Agent
• Agentic Optical Networks
• Hierarchical Multi-Agent Framework
• Lifecycle Management
• Network Automation