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Keep shoppingAI Agent Engineering: Build Production-Ready AI Agents
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242 students
AI Agent Engineering: Build Production-Ready AI Agents
Master AI Agents, RAG, MCP, Memory, Planning, Multi-Agent Systems, LangGraph, CrewAI and LangChain in 21 Days
Created byPankaj Shukla
Last updated 9/2026
English
English [Auto],
What you'll learn
- Build AI agents from scratch using Python and understand the core architecture behind modern AI agents.
- Build AI assistants with conversation memory, tool usage, document processing, and Retrieval-Augmented Generation (RAG).
- Design autonomous AI agents with planning, reasoning, memory, and advanced RAG capabilities.
- Build and integrate AI agents using the Model Context Protocol (MCP) and connect them to external tools and data sources.
- Design and orchestrate multi-agent systems where multiple specialized AI agents work together to solve complex problems.
- Build production-ready AI agents using LangGraph, CrewAI, and LangChain.
- Build a complete AI Research Agent capable of researching information, verifying findings, maintaining research notes, and generating structured reports.
- Deploy AI agent applications and build a complete Research AI Agent Platform as a capstone project.
Explore related topics
Course content
22 sections • 292 lectures • 37h 10m total length
Requirements
- No prior experience with AI agents, RAG, MCP, LangGraph, CrewAI, or LangChain is required. Everything is explained and built step by step.
- Basic familiarity with computers and a willingness to learn programming is enough to get started.
- No advanced Python knowledge is required. The course introduces and explains important Python concepts such as variables, input, lists, functions, loops, and other fundamentals as needed.
- A computer with a stable internet connection is required for following the practical exercises and building the projects.
- You should be willing to write code, experiment, and learn by building real AI applications throughout the 21-day journey.
Description
Welcome to AI Agent Engineering: Build Production-Ready AI Agents — a 21-day practical journey designed to take you from the fundamentals of AI applications to advanced AI agent systems.
Throughout the course, we will build, experiment, and progressively add capabilities such as memory, tools, RAG, planning, MCP, multi-agent systems, orchestration, and deployment. Also we will learn to use Frameworks LangGraph, CrewAI, LangChain to develop AI Agents.
And at the end of the journey, we will bring many of these concepts together to build an AI Research Agent and a Research AI Agent Platform.
What will you learn?
During this 21-day journey, you will progressively learn:
Day 1–6: Build the Foundation
Build your first AI chatbot using Python
Add conversation memory
Learn prompt engineering
Teach AI assistants to use tools
Build applications that can work with documents
Build your first RAG system
Day 7–13: Enter the World of AI Agents
Build your first AI Agent
Understand Model Context Protocol (MCP)
Build autonomous AI agents
Learn advanced agent planning
Explore memory engineering
Build advanced RAG systems
Understand MCP in greater depth
Day 14–15: Advanced & Production-Ready Agents
Understand multi-agent systems
Learn agent orchestration
Explore how to make AI agents production-ready
Day 16–19: Agent Engineering Frameworks
You will work with:
LangGraph
CrewAI
LangChain
The focus is not only on using frameworks, but on understanding how they can be used to build and structure AI agent applications.
Day 18–21: Build the Research AI Agent
You will build an AI Research Agent, explore deployment, and finally bring the concepts from the course together in the capstone project:
Research AI Agent Platform.
Learn by Building
This is a practical course.
Instead of learning concepts in isolation, we progressively build systems and add new capabilities to them.
You will see how individual concepts such as:
LLMs → Memory → Tools → RAG → Agents → Planning → MCP → Multi-Agent Systems
can progressively come together to create more sophisticated AI applications.
The objective is not simply to complete 21 days of videos.
The objective is to develop the understanding and practical experience required to build AI agents yourself.
Who is this course for?
This course is suitable for:
Developers
Software Engineers
Students
AI Enthusiasts
Developers interested in Generative AI
Anyone who wants to learn how AI agents are engineered
You don't need to know everything about AI before starting.
However, you should be comfortable with working on computers.
How should you approach this course?
Don't just watch the videos.
Watch → Code → Experiment → Understand → Build
Pause the videos and write the code yourself.
Experiment with the examples.
Change things.
Try different approaches.
Make mistakes and learn from them.
The goal is not simply to reach Day 21.
The goal is to develop the ability to understand and build AI agent systems.
Start Your AI Agent Engineering Journey
AI is evolving rapidly, with new models, frameworks, protocols, and techniques appearing continuously.
This course is designed to give you a practical foundation in the concepts and engineering approaches behind modern AI agents.
By the end of this journey, you will have explored the major building blocks of AI Agent Engineering and used them to build increasingly sophisticated systems.
So, are you ready?
Start the 21-day journey and let's build AI agents.
Who this course is for:
- Beginners who want to learn how to build AI agents and AI-powered applications from the ground up.
- Python learners and developers who want to move beyond basic programming and start building practical AI systems.
- Software developers and engineers who want to understand modern AI agent architecture, including RAG, memory, planning, MCP, and multi-agent systems.
- AI and machine learning enthusiasts who want hands-on experience building autonomous and production-ready AI agents.
- Students and professionals who want to learn popular AI agent frameworks such as LangGraph, CrewAI, and LangChain through practical projects.
- Anyone who wants to learn by building real-world AI projects, including a complete AI Research Agent and Research AI Agent Platform.