Agentic AI for Scalable and Robust Optical Systems Control
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2026
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| author | Wang, Zehao Han, Mingzhe Cheng, Wei Huang, Yue-Kai Ji, Philip Wu, Denton Safari, Mahdi Holtorf, Flemming AlQubaisi, Kenaish Linke, Norbert M. Zhuo, Danyang Chen, Yiran Wang, Ting Englund, Dirk Chen, Tingjun |
| author_facet | Wang, Zehao Han, Mingzhe Cheng, Wei Huang, Yue-Kai Ji, Philip Wu, Denton Safari, Mahdi Holtorf, Flemming AlQubaisi, Kenaish Linke, Norbert M. Zhuo, Danyang Chen, Yiran Wang, Ting Englund, Dirk Chen, Tingjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20144 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic AI for Scalable and Robust Optical Systems Control Wang, Zehao Han, Mingzhe Cheng, Wei Huang, Yue-Kai Ji, Philip Wu, Denton Safari, Mahdi Holtorf, Flemming AlQubaisi, Kenaish Linke, Norbert M. Zhuo, Danyang Chen, Yiran Wang, Ting Englund, Dirk Chen, Tingjun Systems and Control Artificial Intelligence Networking and Internet Architecture 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. |
| title | Agentic AI for Scalable and Robust Optical Systems Control |
| topic | Systems and Control Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2602.20144 |