What you'll learn
Understand what Agentic AI is and how multi-agent systems work.
Install and configure Python and development tools required for building AI applications.
Create and manage Python virtual environments for AI projects
Configure and securely use OpenAI API keys inside applications.
Integrate live internet search into AI systems using Serper.
Design the architecture of a multi-agent AI system.
Build specialized AI agents with CrewAI.
Create and structure agent roles, goals, and tasks using configuration files.
Develop reusable AI tools and integrations for agent workflows.
Build a Streamlit web interface for interacting with AI agents.
Implement a complete research workflow using collaborating AI agents.
Test and debug an agentic AI system locally.
Use Git and GitHub to manage and publish AI projects.
Create a professional .gitignore configuration for AI applications.
Deploy a working AI agent application to the cloud.
Generate structured reports and outputs from autonomous AI agents.
Build a portfolio-ready AI project demonstrating modern agent development.

Requirements
You do not need prior experience with AI agents or CrewAI to take this course. Everything is explained step-by-step.
You should be comfortable installing software, navigating folders, and running simple commands in a terminal.
You should ideally understand basic Python concepts such as: variables functions installing packages
You will need a computer capable of installing Python and running a small development environment.
Free Accounts for Tools Used in the Course During the course we will create accounts for: OpenAI Serper GitHub Streamlit Cloud

Description
Artificial Intelligence is rapidly evolving beyond simple chatbots. The next major shift is Agentic AI — systems where multiple AI agents collaborate to solve complex tasks.

In this hands-on course, you will learn how to build real-world multi AI agent systems using CrewAI.

Instead of relying on a single AI prompt, you will create a team of specialized agents that research information, analyze findings, and produce structured results automatically.

This course walks you through the entire process step-by-step — from setting up your development environment to deploying a fully working AI application online.

You will start by installing Python, setting up virtual environments, and preparing your development tools. Then you will configure API access for OpenAI and integrate live internet search so your AI agents can retrieve real-time information.

Next, you will design and build a multi-agent architecture using CrewAI. You will create agents with specific roles, goals, and responsibilities, allowing them to collaborate just like a small research team.

As the project develops, you will implement tools, configure workflows, and build a user-friendly interface using Streamlit so users can interact with the system easily.

By the end of the course, you will have built a complete AI research application capable of

• Collecting real-world data
• Analyzing trends
• Generating structured reports
• Exporting results as downloadable PDFs

You will also learn how to test the system locally, manage the project using Git and GitHub, and deploy the application to the cloud so it can be accessed from anywhere.

This course focuses on practical implementation. Instead of just learning theory, you will build a real project that demonstrates how modern AI agent systems work in practice.

If you want to understand the future of AI development and gain hands-on experience building agent-based systems, this course will give you the skills and confidence to start creating your own AI-powered applications.