Will this course help in AI or backend developer interviews?

Yes. It includes real-world implementations and interview-focused topics like MCP architecture, tool calling workflows, and agentic system design.

What will I learn in AI Agents Development course?

You will build MCP servers, implement AI tool calling using Gemini and OpenAI, develop agent controllers, work with vector databases, and create production-ready AI systems.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

MCP Servers & Agentic AI Architecture

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MCP Servers & Agentic AI Architecture

This course is part of Full Stack Agentic AI Specialization

Instructors: Nikhil Agarwal

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build production-ready MCP architecture that powers real AI systems—structure tools, resources, and prompts for scalable execution
Master Gemini and OpenAI tool calling to create AI thatdoesn’tjust respond but executes workflows and delivers real outcomes
Create intelligent agents that automate decisions, trigger backend actions, and handle end-to-end workflows without manual intervention
LeverageChromaDBandpgVectorto build context-aware AI systems using embeddings, retrieval pipelines, and high-performance data flows

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

This course is part of the Full Stack Agentic AI Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

Imagine building an AI that doesn’t wait for prompts but actively runs your backend, selects the right tools, and completes tasks end-to-end.

Most AI applications are limited to generating answers. But real-world systems require structured execution, intelligent workflows, and scalable architecture and that’s where most developers fall behind. In this course, you will build MCP-based AI systems that go beyond responses. You will implement backend logic using services and controllers, build MCP servers, and define tools, resources, and prompts to enable AI to execute in a controlled manner. Get hands-on experience with tool deployments using Gemini and OpenAI, request-response cycles, and agent controllers that cover autonomous workflows. You will also work with vector databases such as ChromaDB and pgVector to enhance context retrieval, accelerate data ingestion, and produce intelligent AI outputs. This Agentic AI course is designed for developers looking to level up and build agent-powered, production-ready systems for real-world industry needs. Stop building AI that just responds start building AI that operates. Enroll now and lead the next wave of intelligent systems.

Learn the MCP server architecture, including tools, resources, prompts, transport layers, and session management. Implement customer and order services to build a strong backend foundation.

What's included

15 videos6 assignments

15 videosTotal 70 minutes

Course Introduction1 minute
Introduction & Cleaning Mock Data5 minutes
Why Controllers Should Not Contain Business Logic & Creating Customer Service5 minutes
Implementing Customer Controller and Query Limit Handling5 minutes
Creating Order Service and Mapping Orders with Customers5 minutes
Order Controller Implementation, Gemini Service Optimization & Final Testing5 minutes
Introduction to MCP Architecture & Standardization4 minutes
MCP Server Components: Tools, Resources, and Prompts4 minutes
Transport Layer, Sessions, and Request Handling4 minutes
Introduction to MCP Components (Tool vs Resource vs Prompt Analogy)5 minutes
Understanding MCP Tools (Actions AI Can Perform)3 minutes
Understanding MCP Resources (Read-Only Data Access)5 minutes
Understanding MCP Prompts (Reusable AI Interaction Templates)4 minutes
Decision Framework: When to Use Tool, Resource, or Prompt5 minutes
Understanding Transport Layer in MCP9 minutes

6 assignmentsTotal 98 minutes

Checkpoint Quiz15 minutes
Checkpoint Quiz15 minutes
Checkpoint Quiz12 minutes
Proficiency Quiz20 minutes
Proficiency Quiz20 minutes
Proficiency Quiz16 minutes

Integrate Gemini and OpenAI tools, implement function calling workflows, and understand why MCP improves tool execution over direct calls. Test and debug AI tool calls for real-world scenarios.

What's included

17 videos8 assignments

17 videosTotal 90 minutes

AI Tool Integration Challenges & Tool Calling Workflow5 minutes
MCP Architecture, Advantages & Comparison with Tool Calling7 minutes
Project Setup for Gemini Tool Calling (Without MCP)5 minutes
Creating Tool Definitions: Weather and Customer Functions4 minutes
Registering Tools in Gemini Configuration5 minutes
Detecting and Executing Tool Calls from Gemini Response4 minutes
Sending Tool Results Back to the LLM for Natural Language Response4 minutes
Testing Tool Calling with Weather and Customer Queries6 minutes
Limitations of Direct Tool Calling and Why MCP is Better6 minutes
Creating OpenAI Service and Installing OpenAI SDK6 minutes
Configuring Environment Variables and Implementing Base OpenAI Functions5 minutes
Understanding OpenAI Responses API and Tool Calling Workflow6 minutes
Defining Tools for Weather and Customer Functions6 minutes
Implementing Function Call Handling with Loop and Switch Case5 minutes
Sending Tool Output Back to OpenAI and Generating Final Response6 minutes
Controller Integration, Instructions Handling, and OpenAI vs Gemini Differences7 minutes
Testing OpenAI Native Tool Calling in Frontend5 minutes

8 assignmentsTotal 119 minutes

Checkpoint Quiz12 minutes
Checkpoint Quiz12 minutes
Checkpoint Quiz15 minutes
Checkpoint Quiz12 minutes
Proficiency Quiz16 minutes
Proficiency Quiz16 minutes
Proficiency Quiz20 minutes
Proficiency Quiz16 minutes

Connect MCP server and client, implement multiple tools, and integrate with LLMs to build agentic workflows. Learn session management, transport logic, and automated tool execution.

What's included

23 videos8 assignments

23 videosTotal 116 minutes

Introduction to MCP and Preparing the Agentic Project3 minutes
Understanding MCP Documentation: Tools, Resources, and Prompts4 minutes
Creating MCP Folder Structure and MCP Server Setup4 minutes
Creating Customer Tools File and Registering First MCP Tool5 minutes
Implementing Input & Output Schema with Zod Validation4 minutes
Adding Second Customer Tool (Get Customer by ID)3 minutes
Creating Order Tools and Implementing Multiple MCP Tools4 minutes
Building Advanced Order Tools (Orders with Customer Details & Order by ID)4 minutes
Creating Weather Tool and Final MCP Tool Registry Setup5 minutes
Setting Up MCP Transport Controller & Session Configuration6 minutes
Creating Transport Logic & Session Management5 minutes
Implementing POST Request for MCP Client–Server Communication6 minutes
Handling GET, DELETE Requests & Configuring Express Routes6 minutes
Architecture Deep Dive: The Critical Role of the MCP Client6 minutes
Introduction & Creating MCP Client Service Structure5 minutes
Implementing Singleton MCP Client and Client Initialization6 minutes
Connecting MCP Client to Server Using HTTP Transport5 minutes
Fetching Available Tools and Implementing Tool Calling Methods5 minutes
Creating Agent Controller and Preparing LLM Request Flow3 minutes
Integrating MCP Client with Gemini for Tool Calling6 minutes
Understanding MCP-to-Tool Architecture and Automatic Tool Execution5 minutes
System Instructions, Prompt Structure, and Temperature Settings5 minutes
Creating Routes, Running the Application, and Testing MCP Tools9 minutes

8 assignmentsTotal 161 minutes

Checkpoint Quiz18 minutes
Checkpoint Quiz21 minutes
Checkpoint Quiz15 minutes
Checkpoint Quiz15 minutes
Proficiency Quiz24 minutes
Proficiency Quiz28 minutes
Proficiency Quiz20 minutes
Proficiency Quiz20 minutes

Integrate MCP with AI models, implement the agentic loop, parse responses, and handle outputs effectively. Build production-ready agentic workflows and optimize system instructions.

What's included

17 videos8 assignments

17 videosTotal 79 minutes

Introduction: Implementing MCP with OpenAI5 minutes
Setting Up OpenAI Service & Installing OpenAI Package4 minutes
Integrating MCP Client with Agent Controller4 minutes
Loading MCP Tools and Preparing Tool Context5 minutes
Writing System Instructions and Response Contract5 minutes
Preparing Messages Structure and Agentic Loop7 minutes
Creating Zod Schema for Tool Intent Validation5 minutes
Parsing OpenAI Responses and Executing MCP Tools6 minutes
Final Output Handling, Loop Completion & Environment Setup7 minutes
Testing MCP Tool Calling with OpenAI GPT Model6 minutes
Interview Prep: MCP Architecture Questions6 minutes
What is an Agentic System?2 minutes
The "Why" - Beyond Simple Chatbots (Why Agentic)1 minute
The Decision Loop - Why Hard-Coded Logic Fails6 minutes
The Agentic Design Pattern - Passing RAG as a Tool5 minutes
Architecture Battle: RAG vs. MCP vs. Agents4 minutes
The Ecosystem: Apps that Support MCP2 minutes

8 assignmentsTotal 137 minutes

Checkpoint Quiz18 minutes
Checkpoint Quiz15 minutes
Checkpoint Quiz9 minutes
Checkpoint Quiz9 minutes
Proficiency Quiz24 minutes
Proficiency Quiz20 minutes
Proficiency Quiz12 minutes
Proficiency Quiz30 minutes

Enable AI memory and retrieval using vector databases like ChromaDB and pgVector. Implement document ingestion, embeddings, and query pipelines for context-aware AI systems.

What's included

18 videos8 assignments

18 videosTotal 86 minutes

The Memory Problem: Why AI Needs Vector Databases?2 minutes
What Vector Databases Do?1 minute
What is ChromaDB?4 minutes
What is pgVector?2 minutes
ChromaDB vs pgVector - When to Choose?4 minutes
Preparing File Structure and Data Assets (PDFs/Docs) for RAG8 minutes
Configuring ChromaDB in Local Environment7 minutes
ChromaDB Client Configuration & Environment Setup5 minutes
VectorStoreChroma Class & Upsert Implementation6 minutes
Search Query Implementation & Vector Store Selection 8 minutes
Introduction to the Data Ingestion Pipeline & Chunking Strategy6 minutes
Implementing the chunkText() Function for Document Chunking4 minutes
Building the ingestFolder() Function & Reading Files6 minutes
Generating Embeddings and Storing Chunks in Vector DB5 minutes
Running the Ingestion Script, CLI Command & Fixing Environment Error7 minutes
Configuring PostgreSQL & pgVector for MacOS10 minutes
Configuring PostgreSQL & pgVector for Windows2 minutes
Summary1 minute

8 assignmentsTotal 115 minutes

Checkpoint Quiz12 minutes
Checkpoint Quiz9 minutes
Checkpoint Quiz15 minutes
Checkpoint Quiz15 minutes
Proficiency Quiz12 minutes
Proficiency Quiz12 minutes
Proficiency Quiz20 minutes
Proficiency Quiz20 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Nikhil Agarwal

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11 Courses2 learners

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157 Courses191,032 learners

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Frequently asked questions

Basic backend knowledge helps, as the course covers controllers, services, API routes, and MCP server implementation using Node.js architecture.

This course is ideal for backend developers, AI engineers, and anyone looking to build scalable, production-ready AI systems with modern architecture.

Absolutely. It emphasizes scalable architecture, modular design, and real integration patterns adopted in modern AI applications.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.