Agentic AI Engineering with MERN: RAG, MCP & AI Agents

<div>Stop building basic chatbots. It is time to build intelligent AI Agents that can actually do things.</div><div><br></div><div>Welcome to the <b><u>Agentic AI Engineering program</u></b>, the only course you need to bridge the gap between simple LLM wrappers and complex, production-ready Agentic Systems.</div><div><br></div><div>Most developers are stuck building "chat with PDF" apps. In this course, we go levels deeper. You will architect a Full-Stack Agentic Application from scratch using React and Node.js, integrating cutting-edge protocols like MCP (Model Context Protocol) and advanced RAG pipelines.</div><div><br></div><div>Why this course? The industry is shifting from "Generative AI" to "Agentic AI." Companies don't just want text; they want Agents that can query databases, execute tools, and perform actions. This course puts you ahead of that curve.</div><div><br></div><div>What you will build: We will engineer a professional-grade AI platform with a modern React UI and a robust Node.js/Express backend. You won't just learn syntax; you will learn the architecture behind autonomous systems.</div><div><br></div><div>Key Technical Deep Dives:</div><div><ul><li><span style="font-size: 1rem;">The Model Context Protocol (MCP): Be one of the first to master this standard. You will build Custom MCP Servers in Node.js to connect your AI to real-world data (like Weather APIs) and expose them as tools to Claude, Gemini, or OpenAI.</span></li><li><span style="font-size: 1rem;">Advanced RAG Pipelines: Move beyond basics. We implement Vector Search using ChromaDB and pgVector, handling embeddings, chunking, and ingestion manually to give you total control.</span></li><li><span style="font-size: 1rem;">Native Tool Calling: Learn how to make LLMs (Gemini & OpenAI) strictly structured JSON to trigger functions in your code—the backbone of Agentic AI.</span></li><li><span style="font-size: 1rem;">Math & Theory: We don't just import libraries. We cover the logic behind Cosine Similarity, Vector spaces, and Retrieval scoring so you understand why your retrieval works.</span></li></ul></div><div><br></div><div>The Tech Stack:</div><div><ul><li><span style="font-size: 1rem;">Frontend: React (Latest), TailwindCSS, Vite</span></li><li><span style="font-size: 1rem;">Backend: Node.js, Express, TypeScript</span></li><li><span style="font-size: 1rem;">AI Models: Google Gemini, OpenAI GPT Models</span></li><li><span style="font-size: 1rem;">Vector Databases: ChromaDB, pgVector (PostgreSQL)</span></li><li><span style="font-size: 1rem;">Protocols: MCP (Model Context Protocol)</span></li></ul></div><div><br></div><div>If you are ready to stop playing with toys and start building intelligent, agentic systems, enroll now. Let’s write some code.</div>

What you'll learn:

• Architect and build a complete Full-Stack (MERN) Agentic AI application using React, Node.js, and Express.
• Implement advanced Retrieval Augmented Generation (RAG) pipelines with embeddings, vector search, and context augmentation.
• Master the Model Context Protocol (MCP) by building custom MCP Servers in Node.js to expose real-world tools to LLMs.
• Build a production-ready Chat Interface in React that handles streaming responses, Markdown rendering, and tool outputs.
• Set up and manage Vector Databases (ChromaDB and pgVector) to store high-dimensional embeddings for semantic search.
• Create Deterministic RAG Systems using JSON and math-based Cosine Similarity to understand the core algorithms of retrieval.
• Implement Native Tool Calling with Gemini and OpenAI to turn natural language into executable code functions.
• Connect your RAG Engine as an MCP Tool, creating a modular system where Agents can "choose" to search your database.