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Artificial intelligence (AI) agents powered by large language models (LLMs) are regarded as a prospective solution for autonomous optical power optimization in optical networks. In this paper, we develop an AI agent that incorporates human expertise, including physical knowledge, optimization knowledge, and optimization workflow. Leveraging provided toolsets, the AI agent explores the configuration space with a digital twin (DT) and deploys selected configurations on the actual network. Following an expertise-guided workflow comprising DT exploration, online evaluation, and fine-tuning, the AI agent performs efficient and reliable optical power optimization in a field-deployed network. Field-trial results show that the expertise-guided AI agent reaches the target quality of transmission (QoT) with an average of only 5.9 adjustments. Under the same scenario, Bayesian optimization (BO) and a genetic algorithm (GA) require 5.8× and 27.9× as many adjustments, respectively. The AI agent also ensures a reliable optimization process by maintaining a Q-factor margin of greater than 4 dB at each adjustment, whereas BO and GA fall below the forward error correction (FEC) threshold. Additionally, the expertise-guided AI agent delivers superior optimization across different wavelength-loading scenarios, demonstrating its capability to generalize across varying channel loads. Consequently, the expertise-guided AI agent provides a promising approach for autonomous, efficient, and reliable optical power optimization in future autonomous optical networks.
Qiu et al. (Thu,) studied this question.