DRL037 Agentic AI - Fundamentals and Applications (Autumn 2026)
- ECTS Credits:
- 5
- Responsible faculty:
- Avdeling for logistikk
- Campus:
- Molde
- Course Leaders:
-
- Swati Aggarwal
- Bjørn Jæger
- Teaching language:
- English
- Study level:
- {code=FU, name=Doctoral degree level}
- Duration:
- 5 days
Table of contents
Contents
This course introduces the foundations of agentic AI systems for students and researchers from diverse disciplinary backgrounds.
Topics include agent architectures, reasoning mechanisms, multi-agent coordination, and domain-focused research applications (including, but not limited to, healthcare and logistics). The course presents conceptual foundations accessible across disciplines while emphasizing applied system design and experimentation. Students will explore both theoretical foundations and practical approaches to adapting agentic AI techniques to their own research and professional domains.
By the end of the course, participants will have a practical understanding of how to design, prototype, and evaluate AI agents and multi-agent systems within their own disciplinary and professional contexts.
The course is connected to the following study programs
Recommended knowledge
The student's learning outcomes after passing the exam
Knowledge
The doctoral student shall have a good overview of
- prompt engineering for agents
- context engineering
- agent architectures (tool design, Plan-and-Execute, reflection)
- task decomposition
- LLM context management and cognitive load management for agents
- multi-agent coordination strategies and failure
- agent training and skills design
- design agent-based systems for real-world domains
- critically evaluate agent performance and risks
- apply reasoning frameworks (CoT, RAG, etc.)
General competence
The doctoral student shall be able to
- identify research gaps in agentic AI
- propose novel agent architectures or improvements
- integrate safety, ethics, and compliance into system design
Forms of teaching and learning
- Lectures (30%)
- Paper discussions (20%)
- Research design & analysis (20%)
- Hands-on design / prototyping / presentations / group discussions (30%)
Coursework requirements - conditions for taking the exam
- Attendance on all five days of the course
- Completion of oral presentation(s) during the course
- Individual report that applies agentic AI concepts to the student’s PhD domain to be submitted within two weeks after the course