Agentic AI for Scalable and Robust Optical Systems Control
🤖 AI Summary
This work addresses key challenges in autonomous control of heterogeneous optical systems—namely, weak task comprehension, difficulty in multi-device coordination, and poor fault tolerance—by introducing AgentOptics, the first AI control framework that integrates an agent-based architecture with the Model Context Protocol (MCP). Through a structured tool abstraction layer, the framework maps natural language instructions to 64 standardized optical operations, enabling end-to-end task orchestration from single devices to full system-level workflows. The contributions include a unified tool interface, a multi-task evaluation benchmark, and integrated mechanisms for natural language understanding, multi-step coordination, error recovery, and closed-loop optimization. Evaluated on 410 tasks, AgentOptics achieves success rates of 87.7%–99.0%, substantially outperforming code-generation baselines (≤50%) and demonstrating robust system-level performance across five real-world scenarios, including DWDM configuration and 5G fronthaul optimization.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optical Systems Control
Autonomous Control
Heterogeneous Devices
Scalability
Robustness
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Model Context Protocol
Autonomous Optical Control
Tool Abstraction Layer
Closed-loop Optimization
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