Mastering Real-World Agentic AI Applications with AG2 (AutoGen)
A comprehensive tutorial series covering the fundamentals to advanced concepts of building agentic AI applications using AG2 (AutoGen).
🎯 Overview
This hands-on workshop series takes you through the complete journey of building intelligent AI agents that can work together, solve complex problems, and integrate with real-world applications. Whether you’re new to agentic AI or looking to deepen your expertise, these modules provide practical examples and industry best practices.
📚 Course Modules
Module 1: Introduction and Foundation of AI Agents
File: module1_introduction/module1_introduction.ipynb
- Understanding the agent paradigm
- Evolution from rule-based to AI-driven agents
- Value of agents in modern applications
- Multi-agent system architecture
Module 2: Setup and Environment Configuration
Directory: module2_setup/
- Setting up AG2 development environment
- Configuration and dependencies
- First agent creation
Module 3: Core Concepts and Architectures
Directory: module3_core_concepts_and_architectures/
- Agent communication patterns
- Message passing and protocols
- System design principles
Module 4: Advanced Agent Design Patterns
Directory: module4_advanced_agent_design_patterns/
- Context-aware routing (
4.1_context_aware_routing.ipynb)
- Escalation mechanisms (
4.2_escalation.ipynb)
- Feedback loops (
4.3_feedback_loop.ipynb)
- Hierarchical structures (
4.4_hirarchical.ipynb)
- Organic agent interactions (
4.5_organic.ipynb)
- Sequential processing (
4.6_sequential.ipynb)
- Redundant systems (
4.7_redundent.ipynb)
- Reasoning agents (
reasoning_agent.ipynb)
Module 5: Building Custom Agents
Directory: module5_building_custom_agents/
- Custom agent development
- Specialized agent behaviors
- Agent personality and capabilities
Directory: module6_integration_with_external_tools/
- Tool calling and API integration
- External system connectivity
- Data processing and analysis
Module 7: Real-World Example
Directory: module7_real_world_examples/
- Complete market analysis application (
marketanalysis/)
- Streamlit integration (
marketanalysis_streamlit/)
- End-to-end implementation
🚀 Getting Started
Prerequisites
- Python 3.9 or higher (Python 3.12+ required for Module 6 MCP integration)
- Basic understanding of Python programming
- Familiarity with AI/ML concepts (helpful but not required)
Installation
- Clone this repository:
git clone <repository-url>
cd ag2-workshop
- Install core dependencies (Modules 1–5, 7):
pip install "ag2[openai]" python-dotenv streamlit tavily-python
- For Module 6 (MCP integration) — requires Python 3.12+:
pip install "ag2[openai,mcp]" python-dotenv streamlit arxiv wikipedia
- Start with Module 1:
jupyter notebook module1_introduction/module1_introduction.ipynb
🔧 Key Technologies
- AG2 (AutoGen): Multi-agent conversation framework
- Streamlit: Web application framework
- Mesop: Modern UI framework
- Azure: Cloud deployment platform
📖 Learning Path
- Beginner: Start with Modules 1-3 to understand fundamentals
- Intermediate: Progress through Modules 4-6 for advanced patterns
- Advanced: Complete Module 7 for real-world production deployments
🎨 Features
- Interactive Notebooks: Hands-on Jupyter notebook tutorials
- Real-World Examples: Practical applications you can deploy
- Multiple Deployment Options: Local development to cloud production
- Comprehensive Patterns: 8 agent design patterns across 7 modules
🤝 Contributing
For questions or improvements, please refer to the main AG2 documentation and community resources.
📚 Additional Resources
This tutorial series provides hands-on experience with cutting-edge agentic AI development using AG2.