Agentic AI for Scalable and Robust Optical Systems Control

TL;DR

This work presents AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol, and establishes it as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems.

Abstract

We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol-compliant actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools across 8 representative optical devices and construct a 410-task benchmark to evaluate request understanding, role-aware responses, multi-step coordination, robustness to linguistic variation, and error handling. We assess two deployment configurations--commercial online LLMs and locally hosted open-source LLMs--and compare them with LLM-based code generation baselines. AgentOptics achieves 87.7%--99.0% average task success rates, significantly outperforming code-generation approaches, which reach up to 50% success. We further demonstrate broader applicability through five case studies extending beyond device-level control to system orchestration, monitoring, and closed-loop optimization. These include DWDM link provisioning and coordinated monitoring of coherent 400 GbE and analog radio-over-fiber (ARoF) channels; autonomous characterization and bias optimization of a wideband ARoF link carrying 5G fronthaul traffic; multi-span channel provisioning with launch power optimization; closed-loop fiber polarization stabilization; and distributed acoustic sensing (DAS)-based fiber monitoring with LLM-assisted event detection. These results establish AgentOptics as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems.

Figures (12)

• Figure 1: Traditional optical device control using ROADM, 400GbE CFP2-DCO, and OSA as examples: (a) Traditional control requires device-specific manuals, custom scripts, and protocol handling. (b) LLM-based control interprets natural-language prompts to generate control code, reducing manual scripting. (c) The proposed AgentOptics framework standardizes control via a unified tool layer, where the MCP client maps prompts to device APIs through tool selection, enabling LLM-based reasoning over tool outputs for more autonomous control.
• Figure 2: Benchmark workflow for evaluating the performance of AgentOptics and the CodeGen baseline, where reference ground truth is established using human-crafted scripts that are manually validated on physical devices for correctness.
• Figure 3: Task success rate achieved by AgentOptics across varying task complexities using three locally hosted and five online LLMs.
• Figure 4: Task success rate achieved by AgentOptics across different task variants using three locally hosted and five online LLMs.
• Figure 5: Task success rate achieved by AgentOptics across varying task complexities using locally hosted and online LLMs, and comparison to the CodeGen baseline that leverages LLM for code generation.