# Agentic AI for Scalable and Robust Optical Systems Control

arXiv: https://arxiv.org/abs/2602.20144 · HTML: https://mlsanity.com/paper/2602.20144/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 24 of 24)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience](https://mlsanity.com/paper/2512.17260/) (2025)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics](https://mlsanity.com/paper/2510.12787/) (2025)
- [Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025](https://mlsanity.com/paper/2509.01659/) (2025)
- [MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization](https://mlsanity.com/paper/2507.19570/) (2025)
- [IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol](https://mlsanity.com/paper/2510.01260/) (2025)
- [First Field Trial of LLM-Powered AI Agent for Lifecycle Management of Autonomous Driving Optical Networks](https://mlsanity.com/paper/2409.14605/) (2024)
- [AI Agents for Photonic Integrated Circuit Design Automation](https://mlsanity.com/paper/2508.14123/) (2025)
- [LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment](https://mlsanity.com/paper/2511.17676/) (2025)
- [First Field-Trial Demonstration of L4 Autonomous Optical Network for Distributed AI Training Communication: An LLM-Powered Multi-AI-Agent Solution](https://mlsanity.com/paper/2504.01234/) (2025)
- [Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework](https://mlsanity.com/paper/2503.17072/) (2025)
- [Fast WDM provisioning with minimal probing: the first field experiments for DC exchanges](https://mlsanity.com/paper/2309.07359/) (2023)
- [Semi-Automatic Line-System Provisioning with Integrated Physical-Parameter-Aware Methodology: Field Verification and Operational Feasibility](https://mlsanity.com/paper/2403.16281/) (2024)
- [ScheduleMe: Multi-Agent Calendar Assistant](https://mlsanity.com/paper/2509.25693/) (2025)
- [Optical Link Tomography: First Field Trial and 4D Extension](https://mlsanity.com/paper/2510.09384/) (2025)

## Built on by (most cited first, 2 of 2)

- [Device Context Protocol: A Compact, Safety-First Architecture for LLM-Driven Control of Constrained Devices](https://mlsanity.com/paper/2605.26159/) (2026)
- [A T-API-Compliant ReAct Agentic Loop for Optical Networks: Generic vs. Domain-Specific Tool Abstractions](https://mlsanity.com/paper/2606.18000/) (2026)

## Abstract (arXiv)

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.