Large language models for optical network O&M: Agent-embedded workflow for automation

TL;DR

This paper proposes a multi-agent architecture integrating large language models into optical network operation and maintenance workflows to enhance automation, efficiency, and intelligent task execution.

Contribution

It introduces a systematic framework for embedding LLM-based agents into optical network O&M, focusing on agent design, prompt engineering, and tool integration for key tasks.

Findings

Demonstrates feasibility of LLM-assisted optical network management tasks.

Proposes a multi-agent collaborative architecture for O&M workflows.

Lays groundwork for future autonomous optical network O&M systems.

Abstract

With the continuous expansion of optical networks and the increasing diversity of services, existing operation and maintenance (O&M) approaches are increasingly challenged to meet the rising demands for intelligence and efficiency. Large language models (LLMs), endowed with advanced semantic understanding and contextual analysis capabilities, are emerging as a promising enabler for intelligent optical network O&M. Recent studies have demonstrated the feasibility of applying LLMs to optical network management, marking an important step toward intelligent automation. However, systematic investigations into how LLMs can be effectively integrated into existing O&M workflows remain limited. This paper addresses this gap by drawing inspiration from best practices in real-world O&M workflows and systematically identifying scenarios that are well suited for LLM integration. We highlight that agent-based design is key to improving the executability of tasks, and we propose a multi-Agent collaborative O&M architecture that integrates LLM capabilities with existing O&M tools. The proposed architecture leverages core LLM-related technologies including prompt engineering and tool invocation, to build Agent solutions targeting key tasks such as optical channel management, performance optimization, and fault management. This work presents a conceptual framework for embedding LLM-based Agents into optical network O&M workflows, forming agentized processes that demonstrate the feasibility of LLM-assisted task execution and lay the groundwork for future autonomous O&M systems featuring closed-loop perception, decision-making, and action.