AI Agent Engineering: Build AI Agents with Aditya Chandak

Welcome to AI Agent Engineering: Build Production-Ready AI Agents in 30 Days — a hands-on learning journey designed to help you progress from AI application fundamentals to building advanced AI agent systems using Python.

Throughout this 30-day journey, you will build practical AI applications and progressively explore LLMs, prompt engineering, conversation memory, tool calling, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), autonomous agents, multi-agent systems, and agent orchestration.

You will also work with popular AI agent frameworks, including LangChain, LangGraph, and CrewAI, and learn how these technologies can be used to design and develop AI-powered applications.

What will you learn?

Days 1–5: AI & LLM Foundations

• Learn essential Python concepts for AI development.
• Connect Python applications to Large Language Models (LLMs).
• Build your first AI chatbot.
• Explore prompt engineering and structured outputs.

Days 6–10: Memory, Tools & RAG

• Implement conversation history and memory.
• Enable AI assistants to call tools and external functions.
• Process documents and extract relevant information.
• Understand embeddings and vector databases.
• Build your first Retrieval-Augmented Generation (RAG) application.

Days 11–15: AI Agents & MCP

• Understand AI agent architecture and execution workflows.
• Build AI agents that use tools and perform multi-step tasks.
• Explore planning, reasoning, and state management.
• Learn Model Context Protocol (MCP).
• Connect AI agents to compatible external tools and data sources.

Days 16–20: Advanced & Multi-Agent Systems

• Explore advanced memory and RAG techniques.
• Build agents capable of handling multi-step tasks.
• Design multi-agent systems with specialized responsibilities.
• Learn agent orchestration, validation, and human-in-the-loop workflows.

Days 21–25: Agent Frameworks & Deployment

• Build AI applications using LangChain.
• Develop stateful agent workflows with LangGraph.
• Explore collaborative AI agents using CrewAI.
• Understand testing, error handling, logging, and deployment fundamentals.

Days 26–30: Build the AI Research Agent Platform

• Design and build an AI Research Agent.
• Integrate research tools, document retrieval, and information gathering.
• Generate structured research reports with source references.
• Develop an application interface.
• Test, deploy, and demonstrate your capstone project.

Learn by building real-world AI projects

This is a practical, project-oriented course. Instead of learning concepts in isolation, you will progressively build AI applications and combine individual capabilities into more sophisticated systems.

By the end of the course, you will have worked on projects involving:

• AI chatbot with conversation memory
• Document question-answering and RAG application
• Tool-using AI agent
• Multi-agent workflow
• AI Research Agent and Research AI Agent Platform

The goal is not simply to learn AI terminology or use existing frameworks. It is to understand the underlying concepts and develop the practical skills needed to build your own AI-powered applications.

Who is this course for?

This course is designed for:

• Beginners interested in Generative AI and AI agents.
• Python developers who want to build LLM-powered applications.
• Software engineers who want to explore modern AI agent architecture.
• Students and fresh graduates looking for hands-on AI projects.
• Data analysts and data engineers interested in AI-powered automation.
• AI enthusiasts who want to learn LangChain, LangGraph, CrewAI, RAG, and MCP.

Prerequisites

No prior experience with AI agents, RAG, MCP, LangChain, LangGraph, or CrewAI is required.

Basic computer knowledge and a willingness to learn programming are sufficient to get started. Python fundamentals will be introduced as needed, although some additional practice may be helpful for complete beginners.

You will need a computer, a stable internet connection, and access to a suitable LLM API or model. Some providers may charge for API usage.

How to get the most out of this course

Follow the learning approach:

Watch → Code → Experiment → Understand → Build

Write the code yourself, experiment with different approaches, debug errors, and improve your applications as you progress through the course.

By the end of this 30-day journey, you will have explored the core building blocks of AI Agent Engineering and developed practical project experience to help you continue building more advanced AI systems.

Start your AI Agent Engineering journey today and learn by building.