Agentic AI Full‑Stack Masterclass: RAG, MCP & AI Agents

Agentic AI Full‑Stack Masterclass: RAG, MCP & AI Agents | Udemy [Update 11/2025]
English | Size: 16 GB
Genre: eLearning

Build production-grade Autonomous Agents with MCP, RAG, Gemini, OpenAI and Signals using Angular & Node.js.

What you’ll learn
Architect and build a complete Full-Stack Agentic AI application using Angular, 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 Angular 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 Static 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 Angular and Node.js architecture.

Stop building basic chatbots. Start building Enterprise-Grade AI Agents.

Welcome to the Agentic AI Engineering Program for Angular Developers.

Most AI courses focus on Python or React. But enterprise applications run on Angular. In this course, you will architect a complete Full-Stack Agentic AI System using Angular (Latest) and Node.js, built with real production architecture in mind.

This is not a toy project. You will design and deploy a scalable AI platform using Clean Architecture, structured services, and real database persistence.

What You Will Build:

A production-ready AI platform featuring:

Real-time LLM token streaming
Structured Tool Calling
Deterministic RAG pipelines
Custom MCP Servers
Production-grade MongoDB integration

You will replace mock data with a fully integrated MongoDB backend, design optimized schemas, insert data via Custom MCP workflows, and implement performant queries for real-world scalability.

Core Technical Deep Dives

Model Context Protocol (MCP):

Build Custom MCP Servers in Node.js and expose internal databases as tools to Google Gemini and OpenAI GPT models.

Angular Signals & AI Streaming:

Handle high-velocity token streams using Angular Signals and RxJS, ensuring smooth UI updates without performance issues.

Advanced RAG Pipelines:

Implement vector search using ChromaDB and pgVector. Manage embeddings, similarity search, and deterministic context augmentation manually.

Native Tool Calling:

Force LLMs to generate strict structured JSON outputs that directly trigger backend logic — the foundation of reliable agent automation.

Production Database Architecture:

Design scalable MongoDB schemas, migrate from mock data to real persistence, and optimize queries for performance.

Tech Stack

Frontend: Angular (Latest), Signals, TailwindCSS
Backend: Node.js, Express, TypeScript (Strict Mode)
Database: MongoDB
Vector Databases: ChromaDB, pgVector (PostgreSQL)
AI Models: Google Gemini, OpenAI GPT
Protocols: Model Context Protocol (MCP)

If you want to move beyond tutorials and start building scalable, intelligent systems with real enterprise architecture, this course is for you.

Who this course is for:

Full-Stack Developers who want to transition from traditional web apps to building AI-powered Agentic systems.
Solutions Architects & Tech Leads who need to implement the new Model Context Protocol (MCP) standard in enterprise.
AI Enthusiasts looking to master the Model Context Protocol (MCP) and modern Tool Calling standards.
Software Engineers who need to implement RAG and Vector Search without relying on “black box” frameworks like LangChain.
Freelancers & Consultants who want to offer high-value “Custom AI Agent” services to clients.
Developers who want hands-on experience designing MongoDB schemas and integrating real database architecture into AI-powered full-stack systems.

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