AI agent for autonomous optical networks: architectures, technologies, and prospects [Invited Tutorial]

The growing demand for high-bandwidth, zero-trouble services is imposing unprecedented challenges on optical communication networks. Traditional human-centric network management approaches are increasingly inadequate for addressing the complexity, scalability, and reliability requirements of modern optical networks. This tutorial provides a comprehensive overview of the evolution toward autonomous optical networks (AONs), where large language model (LLM)-based artificial intelligence (AI) agents are utilized. We systematically introduce the fundamental concepts and architectural frameworks for AI agent-enabled AONs. Key agentic technologies are examined, including domain adaptation strategies for LLMs, advanced prompting techniques, and the construction of agentic AI systems. Furthermore, we analyze the toolsets that support the operational effectiveness of AI agents in AONs. The monitoring and analytics toolset provides accurate awareness of the network state and predicts future changes. The digital twin (DT) construction toolset enables high-fidelity modeling of optical networks. The intelligent management and control toolset is employed for service provisioning, failure management, and continuous network optimization. By integrating these agentic technologies and toolsets, AI agents can deliver end-to-end autonomous network lifecycle management. Key challenges remain in areas such as reliability, proper utilization of the LLM reasoning capabilities, and cost-effectiveness.