This article presents an innovative integrated architecture that combines Large Language Models (LLMs) and Generative Adversarial Networks (GANs) in a continuous feedback loop to address the growing complexity of modern network infrastructure management. The architecture leverages the complementary strengths of these technologies—LLMs for pattern recognition and contextual understanding of network logs, and GANs for realistic simulation of network behaviors—to create a system that evolves through continuous learning. The integration occurs through a specialized middleware layer that facilitates bidirectional information flow, enabling each component to enhance the capabilities of the other. This synergistic relationship results in enhanced diagnostic accuracy, cost-effective solution testing through virtual environments, dynamic adaptation to changing network conditions, and proactive problem identification before service disruptions occur. While implementation challenges exist regarding technical integration, computational requirements, training data availability, and expertise gaps, specific mitigation strategies have demonstrated effectiveness across diverse organizational environments. The architecture represents a significant advancement in network management capabilities, transitioning from reactive troubleshooting to predictive optimization while substantially reducing operational costs and improving service reliability.
Keywords: LLM-GAN integration, Predictive Maintenance, feedback loop architecture, network diagnostics, simulation-based testing
