Agentic Artificial Intelligence

PhD course on LLM-based autonomous agents, multi-agent systems, and advanced RAG architectures.

Instructor: Prof. Dr. Joerg Osterrieder Institution: FHGR - University of Applied Sciences of the Grisons Semester: Spring 2025

Course Modules

Week Topic Materials
1 Introduction to Agentic AI Slides / First Agent
2 LLM Foundations for Agents Slides / Prompting Strategies
3 Tool Use and Function Calling Slides / MCP Tool Implementation/ Function Calling Comparison
4 Planning and Reasoning Slides / Reflexion Implementation
5 Multi-Agent Architectures Slides / Message Passing/ Coordination Demo
6 Agent Frameworks Slides / LangGraph Agent
7 Advanced RAG Slides / Self-RAG Implementation
8 GraphRAG and Knowledge Slides / GraphRAG Implementation
9 Hallucination Prevention Slides / Verification Pipeline
10 Agent Evaluation Slides / Benchmarking Suite
11 Domain Applications Slides / Code Generation Agent
12 Research Frontiers Slides / Generative Agents Demo

Topics by Module

Module Weeks Topics
Foundations 1-2 Agent definitions, ReAct, Chain-of-Thought, prompting
Core Capabilities 3-5 Tool use, MCP, planning, multi-agent systems
Frameworks 6-7 LangGraph, CrewAI, Self-RAG, advanced retrieval
Safety & Evaluation 8-10 GraphRAG, hallucination prevention, benchmarks
Applications 11-12 Domain agents, research frontiers, projects

Resource Description
Slides All 12 lecture PDFs
Quizzes Self-assessment for each week
Glossary 35+ key terms defined
Reference Cards Printable summaries
Code Playground Browser-based Python
GitHub Source repository

Prerequisites

  • Python proficiency (3.10+)
  • Machine learning fundamentals
  • Transformer architecture basics
  • OpenAI or Anthropic API access

Quick Start

pip install langchain langchain-openai python-dotenv
export OPENAI_API_KEY="your-key"

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