This project-based course offers a comprehensive exploration of applying emerging Agentic AI and Foundational Models (FMs), including Large Language Models (LLMs), to computer network management. It combines theoretical foundations with practical assignments, enabling students to develop and implement Agentic AI-driven solutions for challenges such as network automation, anomaly detection, resource optimization, and service orchestration.
The course begins with instructional sessions that briefly introduce the concept of foundational models, cover Agentic AI fundamentals, and highlight their added value for network management through easy-to-grasp case studies. The course then continues with a flipped-learning approach, where students choose topics of interest related to new advancements in the field, such as emerging agentic patterns, multi-agent coordination frameworks, autonomous decision-making and orchestration techniques, and the integration of Agentic AI with network automation platforms, and present their findings to their classmates. Moreover, a significant component of the course is a small-scale project where students will design, program, and implement a solution pertinent to the application of agentic AI in networking or cloud management. Throughout the course, students will engage in hands-on sessions focusing on integrating the implemented Agentic AI/FM models with APIs and existing network platforms, emphasizing the development of innovative management solutions.
- Kursleiter/in: Hamidreza Mazandarani
- Kursleiter/in: Tom Lennard Schockenhoff
- Kursleiter/in: Tarik Taleb