International Journal of Science, Engineering and TechnologyAuthors: Raskar Kasturi Ramesh, Prof. M. R. Kumbhoje, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Modern enterprise, cloud and mobile networks are large, complex and dynamic, and manual monitoring and configuration are slow, error-prone and unable to react quickly to failures, congestion and attacks. AI agents offer an intelligent approach in which software entities use machine learning, reasoning and automation to monitor, analyse and manage network operations with minimal human intervention. This paper reviews AI agents for autonomous network management. It traces the concept from the autonomic computing vision and its monitor–analyse–plan–execute loop, through software-defined networking and intent-based networking, to industry frameworks for zero-touch and autonomous networks. Machine learning techniques for traffic prediction, anomaly and fault detection, reinforcement learning for routing and resource management, graph neural networks for performance modelling, and language-model agents for configuration and troubleshooting are examined. Future directions such as 5G and 6G network automation, open radio access network controllers and network digital twins are discussed. A layered architecture with data collection, analytics, agent decision, orchestration and intent interfaces is proposed, together with research gaps, objectives, a comparison of techniques and a discussion of challenges including trust, safety of automated actions and data quality. The review concludes that AI agents can make networks more adaptive, reliable and secure while reducing operational cost.
DOI: https://doi.org/10.5281/zenodo.24215184
