Agentic AI for Scalable and Robust Optical Systems Control
Authors: Zehao Wang, Mingzhe Han, Wei Cheng, Yue-Kai Huang, Philip Ji, Denton Wu, Mahdi Safari, Flemming Holtorf, +7 more
Organizations: Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA · NEC Laboratories America, Princeton, NJ 08540, USA · Duke Quantum Center and Department of Physics, Duke University, Durham, NC, USA 27708 · Axiomatic AI, Cambridge, MA 02139, USA · Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Joint Quantum Institute, Department of Physics, and the National Quantum Laboratory (QLab), University of Maryland, College Park, MD 20742, USA · Department of Computer Science, Duke University, Durham, NC 27708, USA
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 & tables
Fig. 1: Optical device control using ROADM, 400 GbE 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 remote distributed device control and scalable integration of new devices.
Lumentum 400 GbE CFP2 digital coherent optics (CFP2-DCO)
6
Set center frequency/output power/operation mode; get config, …
Optilab LT-12-E-M ARoF Tx
6
Set bias voltage/current; get status, …
APEX Technologies OSA
26
Get power/spectrum; set/get measurement parameters, …
Calient S320 Optical Circuit Switch
4
Get port; add/delete connection; delete all connections
DiCon Microelectromechanical Systems (MEMS) 32 × 32 Optical Switch
2
Get connections; set connection
TABLE I: List of optical devices and validated MCP tools supported by AgentOptics.
Fig. 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.
Type
Description
Task example
Paraphrasing
Same meaning, different phrases
• Operate the CFP2 so that port cfp2-opt-1-1 has an output target power setting of − 5 dBm. • Using the CFP2, adjust the output target power parameter on port cfp2-opt-1-1 to − 5 dBm.
Non-sequitur
Adding unrelated information to the task
• Set CFP port cfp2-opt-1-1 power to − 5 dBm; the bench mat has a curled corner.
Error
Task with wrong or lost value
• Missing power value: on the CFP2, set output target power on port cfp2-opt-1-1. • Wrong power value: on the CFP2, set output target power on port cfp2-opt-1-1 to − 100 dBm.
Chain
Sequential related tasks
• First set CFP2 port cfp2-opt-1-1 output target power to − 4 dBm, then read CFP2 output power.
Roles
Task tone as service provider or user
• You are an optical device user; set CFP port cfp2-opt-1-1 power to − 5 dBm.
TABLE II: Five representative task variants evaluated in the agentic optical device control benchmark.
Fig. 3: Task success rate achieved by AgentOptics across varying task complexities using five locally hosted Gemma and Llama models and four online LLMs. The two-action bar aggregates direct and chained two-action tasks.
Fig. 4: Task success rate achieved by AgentOptics across different task variants using five locally hosted Gemma and Llama models and four online LLMs.
Fig. 5: Task success rate achieved by AgentOptics using AgentOptics-Local (Gemma-4-12B) and AgentOptics-Online (Claude Sonnet 4.5), compared with the CodeGen baseline. The two-action bar aggregates direct and chained two-action tasks.
Fig. 6: Task success rate achieved by AgentOptics using AgentOptics-Local (Gemma-4-12B) and AgentOptics-Online (Claude Sonnet 4.5), compared with the CodeGen baseline.
Fig. 7: Trade-off between success rate and average cost for the combined direct dual-action and chained-task subset with AgentOptics and the CodeGen baseline using locally hosted (squares) and online (circles) LLMs. Marker size indicates the relative average execution time.
Calls an invalid function (e.g., calls AP2XXX.get_powower , which does not exist).
CodeGen
Import non-existing library
11.18%
Imports an undefined library (e.g., import lab_api , which is undefined).
AgentOptics
Wrong tool
89.09%
Calls the wrong tool (e.g., arof_get_power instead of arof_read_power ).
AgentOptics
Missing tool
10.91%
Required tools are not invoked (expected OSA-related tools, but called none).
TABLE III: Reasons and examples for CodeGen and MCP-based AgentOptics execution failures. The percentages are calculated with respect to the total number of failures within each approach.
Fig. 8: Representative OSA MCP tool onboarding workflows for manual and LLM-assisted MCP server generation.
Fig. 9: Diagram for DWDM link configuration with ARoF and 400 GbE signals.
Fig. 10: (a) AgentOptics workflow for LLM-assisted wide-bandwidth ARoF 5G new radio (NR) link with an RFSoC ZCU216 board and an ARoF transmitter-receiver pair. (b) and (c) Optimized ARoF transmitter bias voltage across link SNR and BER with different modulation orders, where the vertical dashed line indicates the optimized bias voltage selected by AgentOptics.
Fig. 11: (a) AgentOptics provisions a 400 GbE channel in a two-span link and autonomously optimizes the channel GSNR based on a single-line human language instruction. (b) Autonomous launch power optimization of the CFP2-DCO 400 GbE transmitter (Tx) performed by AgentOptics. (c) Pre-FEC BER optimization of the 400 GbE signal by AgentOptics using an online LLM (Sonnet 4.5) without impacting existing background traffic.
Fig. 12: (a) Experimental setup and control architecture for fiber link polarization stabilization using AgentOptics. (b) Closed-loop polarization stabilization results with deliberate fiber perturbations, showing polarization state and piezo controller actuation over time. Red dashed vertical lines indicate the perturbation times.
Fig. 13: (a) Experimental setup and workflow for AgentOptics-enabled fiber monitoring using DAS. (b) LLM-based reasoning and prompt engineering (PE) for automated event interpretation on the DAS waterfall plot analysis for (c) a stable environment, (d) human-induced pseudo fiber agitation, and (e) a real fiber cut event.
IDLab, Department of Information Technology at Ghent University - imec, Ghent, Belgium · Department of Electrical and Computer Engineering, Princeton University, USA