Topics:

• Complex workflow patterns
• Branching and conditional logic
• Error handling and recovery
• Workflow composition

07 - Human In The Loop

Integrate human feedback into AI workflows for enhanced decision-making.

Topics:

• Human-in-the-loop architecture
• Interruption and resumption patterns
• Feedback collection and integration
• Interactive AI systems

08 - RAG With LangGraph

Build Retrieval-Augmented Generation systems using LangGraph.

Topics:

• RAG fundamentals
• Document retrieval and indexing
• Integration with LangGraph
• Question-answering systems

09 - VectorLess RAG

Explore modern RAG approaches that don't rely on vector databases.

Topics:

• Alternative retrieval methods
• Keyword-based and semantic search
• Hybrid retrieval strategies
• Scalability and efficiency

10 - Guardrails

Implement safety mechanisms and validation in AI systems.

Topics:

• Input/output validation
• Prompt injection prevention
• Content filtering
• Compliance and safety patterns

🛠️ Prerequisites

• Python 3.8+ - Core programming language
• Jupyter Notebook - Interactive learning environment
• pip - Python package manager
• Basic Python knowledge - Variables, functions, classes
• Understanding of LLMs - Familiarity with large language models

📦 Installation

1. Clone the repository:

git clone https://github.com/mdzaheerjk/Complete-Agentic-AI-Bootcamp-2026.git
cd Complete-Agentic-AI-Bootcamp-2026

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Launch Jupyter:

jupyter notebook

🚀 Getting Started

1. Start with 01 Pydantic in Python to understand data validation
2. Move through modules sequentially as they build on each other
3. Experiment with the code examples in each notebook
4. Try modifying examples and running variations

💻 Tech Stack

• Pydantic - Data validation and settings management
• LangGraph - Agentic workflow orchestration
• LangChain - LLM and tool integrations
• Python - Core language
• Jupyter - Interactive notebooks

📖 Learning Format

Each module includes:

• 📓 Jupyter Notebooks - Interactive code examples and explanations
• 💡 Practical Examples - Real-world use cases
• 🧪 Hands-on Exercises - Reinforce learning through practice
• 📝 Notes and Comments - Detailed explanations in code

🎓 Who Is This For?

• AI/ML Engineers - Building production AI systems
• Python Developers - Transitioning to AI development
• Data Scientists - Implementing autonomous systems
• Researchers - Exploring agent-based architectures
• Students - Learning modern AI development

🤝 Contributing

Contributions are welcome! If you find issues or have suggestions:

1. Open an issue to discuss your idea
2. Fork the repository
3. Create a feature branch
4. Submit a pull request

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

📞 Support

• Check existing notebooks for examples
• Review code comments for clarifications
• Open an issue for bugs or questions
• Refer to official documentation links in notebooks

🔗 Resources

• LangGraph Documentation
• Pydantic Documentation
• LangChain Documentation
• Python Official Docs

📅 Updates & Roadmap

This bootcamp is actively maintained and updated to reflect the latest developments in Agentic AI. Check back regularly for new content and improvements.

🌟 Acknowledgments

Built with passion for the Agentic AI community. Special thanks to all contributors and learners making this resource better.

Happy Learning! 🚀

Last Updated: June 2026