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Home Ai Courses

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

January 15, 2026
in Ai Courses, Free Course, Other Tutorials, Tutorials
Agentic AI Engineering with MERN: RAG, MCP & AI Agents
Source
https://www.udemy.com/course/agentic-ai-engineering-with-mern-rag-mcp-ai-agents/
Category
Ai Courses
File Size
21.8 GB
Publisher
Nikhil Agarwal
Updated
January 15, 2026
Description

Stop building basic chatbots. It is time to build intelligent AI Agents that can actually do things. Welcome to the Agentic AI Engineering program, the only course you need to bridge the gap between simple LLM wrappers and complex, production-ready Agentic Systems. 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.

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.

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.

Key Technical Deep Dives:

  • 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.
  • 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.
  • Native Tool Calling: Learn how to make LLMs (Gemini & OpenAI) strictly structured JSON to trigger functions in your code—the backbone of Agentic AI.
  • 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.

The Tech Stack:

  • Frontend: React (Latest), TailwindCSS, Vite
  • Backend: Node.js, Express, TypeScript
  • AI Models: Google Gemini, OpenAI GPT Models
  • Vector Databases: ChromaDB, pgVector (PostgreSQL)
  • Protocols: MCP (Model Context Protocol)

If you are ready to stop playing with toys and start building intelligent, agentic systems, enroll now. Let’s write some code.

Preview
1 image
Preview
Download Files
22 parts
Part 11000.0 MB
Part 21000.0 MB
Part 31000.0 MB
Part 41000.0 MB
Part 51000.0 MB
Part 61000.0 MB
Part 71000.0 MB
Part 81000.0 MB
Part 91000.0 MB
Part 101000.0 MB
Part 111000.0 MB
Part 121000.0 MB
Part 131000.0 MB
Part 141000.0 MB
Part 151000.0 MB
Part 161000.0 MB
Part 171000.0 MB
Part 181000.0 MB
Part 191000.0 MB
Part 201000.0 MB
Part 211000.0 MB
Part 22819.8 MB
Total: 21.3 GB.rar
Tags: Ai Coursesfree courseOther TutorialsTutorials
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