DSC627 Agentic AI

[2–0, 2 cr.]

This course introduces the principles and practices of building autonomous, goal-driven AI agents powered by large language models (LLMs). Students will explore core frameworks for agent development, context engineering to enhance performance and reliability, and function calling to enable task execution. The course covers the design of single and multi-agent systems using tools such as AutoGen, as well as agent memory management through vector stores, episodic memory, and retrieval-augmented generation (RAG). Learners will also study strategies for memory and knowledge compression to optimize agent behavior. Hands-on projects equip students to develop robust, autonomous AI agents for applications in conversational AI, intelligent automation, and enterprise solutions.