End-to-End Agentic AI Automation Lab

Welcome to the official repository for End-to-End Agentic AI Automation Lab, a comprehensive and hands-on project portfolio developed as part of the Agentic AI and GenAI v2.0 course.

This repository showcases real-world projects and advanced implementations of agentic AI systems, multi-agent frameworks, RAG pipelines, and AI workflow automation. It is designed for developers, researchers, and enthusiasts interested in building, deploying, and managing intelligent AI agents at scale.

📄 About the Course

This work is based on the curriculum from Agentic AI v2.0, which provides in-depth knowledge and practical experience with:

• LangChain, LangGraph, LangFlow
• CrewAI, AutoGen, Agno
• Retrieval-Augmented Generation (RAG), Adaptive RAG
• Workflow automation with n8n
• Monitoring tools: LangSmith, Opik, ClearML
• Deployment tools: GitHub Actions, Docker, AWS, BentoML
• Model Context Protocol (MCP) for standardized tool and data integration

📈 Features Covered

• ✅ AI Agent Frameworks (LangChain, LangGraph, CrewAI, Agno, AutoGen)
• ✅ Multi-Agent Collaboration & Memory Management
• ✅ LangFlow UI-based App Building
• ✅ Adaptive & Agentic RAG Systems
• ✅ Model Context Protocol (MCP) Integration
• ✅ End-to-End Deployment with CI/CD
• ✅ Monitoring, Debugging & Human Feedback Integration
• ✅ Cloud-Native Deployment using AWS, Docker
• ✅ Real-World Agentic AI Use Cases (Chatbots, Financial Agents, Automation)

🎓 Learning Objectives

By exploring this repository, you will:

• Understand the architecture of agentic AI systems
• Gain experience with LLM orchestration tools
• Build scalable and intelligent multi-agent applications
• Learn how to automate and monitor AI workflows
• Integrate standardized protocols like MCP into real-world AI pipelines

🏃‍♂️ Getting Started

To clone the repository:

git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git

Each folder will contain:

• README.md with module overview
• notebooks/ or scripts/ for implementations
• configs/ for deployment & environment setup

📊 Tech Stack

• Languages: Python
• Frameworks: LangChain, LangGraph, CrewAI, AutoGen
• Orchestration: n8n, LangFlow
• Deployment: GitHub Actions, Docker, AWS EC2/S3/ECR, BentoML
• Monitoring: LangSmith, Opik, ClearML
• Databases: FAISS, ChromaDB, vector stores
• Protocols & Standards: Model Context Protocol (MCP)

🌐 Licensing

This project is licensed under the MIT License.

📢 Final Notes

This repository reflects a complete and evolving body of work in agentic AI systems and automation. Contributions, suggestions, and forks are welcome as part of the open-source learning community.

For questions or collaborations, feel free to reach out via GitHub Issues.