Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
16 Hour(s) 41 Minute(s)
Language
English
Taught by
Nikhil Agarwal
Rating
Course Overview
Build production-grade Autonomous Agents with Model Context Protocol (MCP), RAG, and Tool Calling using the MERN Stack.
Stop building basic chatbots. It is time to build Intelligent AI Agents that can actually take action.
Welcome to the Agentic AI Engineering Program, the complete guide to moving from simple LLM wrappers to production ready Agentic Systems.
Most developers are stuck building chat with PDF apps. In this course, you go far beyond that. You will architect a full stack Agentic AI Application from scratch using React and Node.js, integrating modern standards like MCP (Model Context Protocol) and advanced RAG pipelines.
Why This Course Matters
The industry is shifting from Generative AI to Agentic AI.
Companies no longer want models that only generate text. They want systems that can:
Query databases
Execute tools
Trigger backend functions
Perform real world actions
This course positions you ahead of that shift.
What You Will Build
You will engineer a professional grade AI platform with:
A modern React UI
A scalable Node.js and Express backend
Real time LLM integrations
Structured Tool Calling
Production level Database Architecture
This is not about syntax. This is about designing the architecture behind autonomous systems.
Key Technical Deep Dives
Model Context Protocol (MCP)
Build Custom MCP Servers in Node.js that connect your AI to real world data sources and expose them as tools to Google Gemini and OpenAI GPT models.
Advanced RAG Pipelines
Implement Vector Search using ChromaDB and pgVector while handling embeddings, chunking, ingestion pipelines, and retrieval control manually.
Native Tool Calling
Force LLMs like Gemini and OpenAI GPT to return structured JSON outputs that trigger backend functions. This is the foundation of reliable Agentic Automation.
Math and Core Theory
Understand Cosine Similarity, Vector Spaces, Retrieval Scoring, and context ranking logic so you know why your system works, not just how to call an API.
Production Database Architecture
Design scalable MongoDB schemas, migrate from mock data to real persistence, and implement optimized queries for performance and scalability.
Tech Stack
Frontend: React (Latest), TailwindCSS, Vite
Backend: Node.js, Express, TypeScript
Database: MongoDB
Vector Databases: ChromaDB, pgVector (PostgreSQL)
AI Models: Google Gemini, OpenAI GPT Models
Protocols: Model Context Protocol (MCP)
If you are ready to stop building toy demos and start building intelligent, scalable Agentic systems, this course is for you.
Let’s build something real.
Course Content
- 11 section(s)
- 97 lecture(s)
- Section 1 Course Introduction: The Landscape of Agentic AI - RAG, MCP, and the Future
- Section 2 Agentic Architecture Setup & API Security
- Section 3 Deep Dive into RAG: Architecture, Embeddings & Vector Search SEO
- Section 4 Build a Frontend AI Architecture: React, NodeJS & Gemini API
- Section 5 Deterministic RAG Q&A Bot - JSON, Embeddings & Cosine Similarity
- Section 6 Building a Custom MCP Server - NodeJS, Weather API & Tool Calling
- Section 7 Building AI Agentic Systems (Part 1): Architecture & Patterns
- Section 8 Understanding Vector Databases
- Section 9 Building AI Agentic Systems (Part 2) - Vector DB Pipeline Engineering
- Section 10 Building AI Agentic Systems (Part 3) - MCP + RAG (pgvector & ChromaDB) + AI LLMs
- Section 11 MongoDB Integration and Production Data Architecture
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., Implement MongoDB integration from schema design to optimized query execution within a production-grade React and Node.js architecture.
Skills covered in this course
Reviews
-
AAngga Pratama
very nice for newbie
-
pp uma
Just worth itt!!!!! Literally worthh the course, every topic was covered in detail
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UUdemy User
this is course is very excellent
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SSujal agarwal
Loved the course, just started watching now, and can anticipate the quality of content, thanks Nikhil sir
