AI Agents with Python and CrewAI

About this course

Large Language Models can answer questions, but they can't act on their own. They can't search the web for current information, query a database, send an email, or trigger a notification. This course bridges that gap. You will learn how to build autonomous AI agent systems that don't just think — they do.

We start with the foundations: what milestones shaped modern AI, how we got from simple language models to agentic AI, and what distinguishes an AI agent from a chatbot. You will understand the key concepts — roles, goals, tools, tasks, and crews — and learn why multi-agent orchestration is one of the most powerful design patterns in applied AI today.

The core of this course is CrewAI, a Python framework for building collaborative multi-agent systems. You will go from zero to fully functional agent pipelines through three hands-on use cases, each building on the previous one:

In Use Case 1 (Tool Integration), you experience the knowledge cutoff problem firsthand, then solve it by giving agents access to web search via SerperDevTool. You also build your first custom tool — an email sender using the Brevo API — and orchestrate a two-agent system where one agent researches and another sends the results by email.

In Use Case 2 (Database Monitoring & Automated Reporting), you build custom tools for SQLite queries and Excel report generation. Two specialized agents work together in a sequential pipeline: a Database Specialist identifies products with low inventory, and a Reporting Specialist generates an Excel report and sends an email notification. This use case also demonstrates working with alternative LLMs like DeepSeek.

In Use Case 3 (Web Scraping & Multi-Channel Notifications), you build a three-agent system that scrapes book data from a website using Selenium, sends a professional summary email, and triggers an SMS confirmation. This is a complete multi-agent, multi-channel automation pipeline.

By the end of this c

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