Course Syllabus
Agentic Artificial Intelligence
Course Code: AAI-PhD Semester: Spring 2025 Credits: 6 ECTS Format: Weekly lectures + hands-on labs
Course Description
This doctoral-level course provides a comprehensive introduction to agentic artificial intelligence - autonomous systems that leverage large language models to reason, plan, use tools, and collaborate. Students will learn both theoretical foundations and practical implementation skills.
Learning Outcomes
Upon completion, students will be able to:
- Design agent architectures for complex reasoning tasks
- Implement tool-using agents with multiple LLM providers
- Build multi-agent systems with effective coordination
- Evaluate agent performance using standard benchmarks
- Apply safety measures to prevent hallucinations
- Create domain-specific agents for real-world applications
Prerequisites
- Strong Python programming skills
- Familiarity with machine learning concepts
- Basic understanding of transformer architectures
- Ability to read and understand research papers
Required Materials
- Access to OpenAI or Anthropic API (student credits available)
- Python 3.11+ development environment
- GitHub account for coursework submission
Schedule
| Week | Topic | Deliverables |
|---|---|---|
| 1 | Introduction to Agentic AI | Lab 1: First Agent |
| 2 | LLM Foundations for Agents | Lab 2: Prompting Strategies |
| 3 | Tool Use and Function Calling | Lab 3: MCP Implementation |
| 4 | Planning and Reasoning | Lab 4: Reflexion Agent |
| 5 | Multi-Agent Architectures | Lab 5: Agent Team |
| 6 | Agent Frameworks | Lab 6: Framework Comparison |
| 7 | Advanced RAG | Lab 7: Self-RAG |
| 8 | GraphRAG and Knowledge | Lab 8: Knowledge Graph |
| 9 | Hallucination Prevention | Lab 9: Fact Checker |
| 10 | Agent Evaluation | Lab 10: Benchmark Suite |
| 11 | Domain Applications | Lab 11: Domain Agent |
| 12 | Research Frontiers | Final Project Presentation |
Assessment
| Component | Weight | Description |
|---|---|---|
| Weekly Labs | 40% | Hands-on implementation exercises |
| Paper Presentations | 20% | Critical analysis of assigned papers |
| Final Project | 30% | Novel agent system development |
| Participation | 10% | Class discussions and peer review |
Grading Scale
| Grade | Percentage |
|---|---|
| A | 90-100% |
| B | 80-89% |
| C | 70-79% |
| D | 60-69% |
| F | Below 60% |
Policies
Late Submissions
- Labs accepted up to 48 hours late with 20% penalty
- No late submissions for final project
Academic Integrity
- All code must be original or properly attributed
- Use of LLM assistants must be disclosed
- Collaboration encouraged but individual submissions required
Accommodations
- Students needing accommodations should contact the instructor within the first week
Office Hours
Instructor: Prof. Dr. Joerg Osterrieder Email: joerg.osterrieder@fhgr.ch Office Hours: By appointment via email
Resources
This syllabus is subject to change. Students will be notified of any modifications.