AI Agents with Python and CrewAI

AI Agents with Python and CrewAI. This course explores how to build autonomous intelligent agents using Python and the CrewAI framework to automate processes, search the web, communicate with databases, and send messages. Large Language Models (LLMs) can answer questions, but they don’t have the ability to take independent actions. They can’t search the web for up-to-date information, query a database, send an email, or trigger a notification message. This course fills that gap by teaching you how to build autonomous AI agent systems—systems that can perform practical tasks in addition to analyzing and thinking. The course starts with the basics, showing what stations have shaped modern AI, how we got from simple language models to agent-based AI, and the differences between an AI agent and a simple chatbot. Key concepts such as roles, goals, tools, tasks, and groups are explored to demonstrate why multi-agent coordination is one of the most powerful design patterns in applied AI today. The main part of the course focuses on the CrewAI framework, a Python framework for building collaborative multi-agent systems. Participants will experience going from scratch to building fully functional pipelines through three practical scenarios. In the first scenario, the problem of knowledge constraints on models is investigated and solved by giving them web search access via the SerperDevTool; a custom email sending tool is also built with the Brevo API to design a two-agent system where one agent does the research and the other emails the results. In the second scenario, custom SQLite query and Excel report generation tools are created to allow two specialized agents to collaborate to check inventory and send reports; this section also teaches how to work with alternative models such as DeepSeek. In the third scenario, a three-agent system is designed that extracts web data with Selenium, sends email summaries, and sends confirmation SMS. By the end of the course, learners will fully understand how to define agents, build custom tools, coordinate multi-agent workflows, and connect external services through Jupyter Notebooks.