AI Agent Engineering: Build Production-Ready AI Agents

Descripción

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.

Lo que aprenderás

• 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.

Requisitos

• 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.