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Explore how generative AI and Large Language Model (LLM) agents can revolutionize network automation and potentially replace human network operators in this 21-minute conference talk from NANOG. Discover the technical challenges and innovative approaches to implementing AI-powered network troubleshooting systems that go beyond traditional automation scripts. Learn about multi-agent configurations where multiple LLM agents collaborate to make autonomous decisions, plan investigation procedures, and adapt strategies based on real-time outcomes during network troubleshooting scenarios. Examine the limitations of current LLMs like GPT-4o in handling specific network device specifications and understand how in-context learning approaches can improve operational accuracy. Gain insights into the practical implementation of AI agents for complex network operations that require dynamic decision-making rather than simple question-and-answer interactions, presented by Ryosuke Sato from NTT FIELDTECHNO CORPORATION as part of ongoing efforts to maintain large-scale networks with limited human operators.
