A YANG-Grounded LLM Agent Supporting Multi-Vendor OpenROADM Optical Transport Networks
The growing role of optical networks in Artificial Intelligence (AI) infrastructure exposes AI researchers to a field still gated by fluency with vendor-specific Yet Another Next Generation (YANG) models and Network Configuration Protocols (NETCONF). Large Language Models (LLMs) can help bridge this gap if applied properly. Specifically, LLMs do not know the YANG data models adopted by commercial Open Reconfigurable Optical Add-Drop Multiplexer (OpenROADM) equipment, and retrieving YANG as flat text loses the hierarchical structure that gives each constraint its meaning. This paper presents an LLM agent that extends the Optical Network Robotic Automation Platform (ON-RAP) with YANG-Grounded Agentic Retrieval-Augmented Generation (RAG). The agent parses 154 OpenROADM YANG files into hierarchically structured chunks, drives retrieval on demand through a Reasoning-and-Acting loop, and routes YANG constraints to the model through three complementary paths spanning prompt-time injection, tool-call retrieval, and post-generation validation. The system is evaluated against retrieval baselines on an open-source Lightynode simulated testbed and was demonstrated live on a commercial multi-vendor testbed at the Optical Fiber Communication Conference 2026. The same pipeline drives both testbeds without modification. OpenROADM serves as the first instantiation of a pipeline whose YANG-aware components apply to other open standards built on YANG and NETCONF.