Agentic AI for Scalable and Robust Optical Systems Control

Abstract

We present AgentOptics, a language-driven orchestration layer for high-fidelity, autonomous optical system control built upon the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes standardized, schema-validated actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools spanning eight representative optical devices and construct a comprehensive 410-task benchmark to evaluate the performance of AgentOptics across request understanding, role-dependent responses, multi-step coordination, robustness to linguistic variation, and error-handling capability. We evaluate two deployment configurations, integrating either commercial online large language models (LLMs) or locally deployed open-source LLMs, and compare against LLM-based code generation baselines. Experimental results demonstrate that AgentOptics achieves average task success rates from 65.4% to 98.8% across the evaluated local and online models, significantly outperforming code generation approaches with up to only 55% success rate. We further validate the broader applicability of AgentOptics through five representative case studies covering dense wavelength division multiplexing (DWDM) link provisioning, analog radio-over-fiber (ARoF) link characterization, multi-span channel optimization, polarization stabilization, and distributed acoustic sensing (DAS)-based fiber monitoring with LLM-assisted event interpretation. We also demonstrate an LLM-assisted workflow that generates MCP-compatible tool interfaces from legacy device-control code, illustrating a scalable path for onboarding additional optical device capabilities. These results demonstrate that AgentOptics provides a scalable and robust interface for natural-language-driven control and orchestration of heterogeneous optical devices and systems.