Build Ai Agents - Complete Learning Journey

A comprehensive end-to-end tutorial series covering the complete lifecycle of building, deploying, and monitoring AI agents using modern GenAI frameworks.

🎯 Learning Path

📚 1. LangGraph Introduction

Learn the fundamentals of LangGraph - a powerful framework for building stateful, multi-actor applications with LLMs. Master concepts like:

• Graph-based agent architectures
• State management and message flow
• Tool calling and conditional routing
• Building custom agent workflows

🧠 2. Knowledge Assistant

Build intelligent knowledge retrieval systems that can:

• Query vector databases and document stores
• Implement Retrieval-Augmented Generation (RAG)
• Create context-aware responses
• Handle complex information retrieval tasks

🔧 3. MLflow Integration for GenAI Apps

Integrate MLflow for production-ready AI applications:

• Model lifecycle management
• Experiment tracking and versioning
• ResponsesAgent integration
• Production model serving

🚀 4. End-to-End Project

Combine all concepts into a complete AI agent system:

• Multi-tool agent architecture
• Real-world business logic integration
• Complex workflow orchestration
• Production-ready implementation

🌐 5. Deploy GenAI App as Web App

Deploy your AI agents using Databricks Apps:

• Streamlit-based chat interfaces
• Real-time streaming responses
• User feedback collection
• Custom UI and branding

📊 6. Evaluation & Monitoring

Implement comprehensive evaluation and monitoring:

• MLflow evaluation harness
• Quality metrics and LLM judges
• Performance monitoring
• Continuous improvement workflows

🛠️ Technologies Used

• LangGraph - Agent framework
• Databricks - ML platform and serving
• MLflow - Model lifecycle management
• Streamlit - Web interface
• Unity Catalog - Function tools and vector search

📖 Additional Resources

• Databricks Chat App Deployment
• MLflow Evaluation & Monitoring

Start your journey by exploring each folder in order - from basic concepts to production deployment!