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End-to-End-Agentic-Ai-Automation-Lab

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

#agentic-ai#agentic-rag#auto-gen#aws#docker#google-adk

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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