Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
15 Hour(s) 18 Minute(s)
Language
English
Taught by
Nikhil Agarwal
Rating
Course Overview
Build production-grade Autonomous Agents with MCP, RAG, Gemini, OpenAI and Signals using Angular & Node.js.
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.
Course Content
- 11 section(s)
- 88 lecture(s)
- Section 1 Course Introduction: The Landscape of Agentic AI - RAG, MCP, and the Future
- Section 2 Setting up the Environment
- Section 3 Deep Dive into RAG: Architecture, Embeddings & Vector Search SEO
- Section 4 Build a Modern AI Chatbot: Angular, NodeJS & Gemini API
- Section 5 Static RAG Q&A Bot - JSON, Embeddings & Cosine Similarity
- Section 6 Building a Custom MCP Server: Node.js, 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 Database Bootcamp
- 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 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.
Skills covered in this course
Reviews
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DDivya Bhalla
Explaining in multiple ways so that concept is clear. Beautiful explanation of Semantic search - "cheap car" and "afordable vehicle". PPT's are very good and clear, also shared as pdf which is v helpful. Has broken down concepts beautifully and structured it well such that concepts get well embedded in mind and is not an overload.
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GGodithi Umesh
Thank you for your wonderful effort in explaining the concepts of AI that make the full stack able to keep pace with the current AI era
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pp uma
The amazing course I needed, specially now. Thank you sir for making AI agents concept so easy. looking forward for more AI Courses. I completed it within 1.5 day. It was so interavtive.
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YYanbo Guan
I would say it will be a great start if you want to take the fisrt step to vibe coding + MCP + RAG
