Books & Courses
Building AI Agents & Agentic AI System via Microsoft Autogen
Description
Welcome to “Building AI Agents and Agentic AI Systems Using AutoGen”, a hands-on, project-driven course designed to help you master the future of intelligent software: Agentic AI. As large language models (LLMs) become more powerful, the next evolution is enabling them to work collaboratively through AI agents—and this course is your complete guide to making it happen using Microsoft’s AutoGen framework.
Whether you’re a data scientist, ML engineer, AI researcher, or product builder, this course will take you step-by-step into the world of multi-agent AI systems. You’ll learn to design, build, and deploy AI agents that can autonomously plan, reason, and execute complex tasks by communicating with each other and interacting with external tools.
What you’ll learn:
- Understand the fundamentals of Agentic AI and how it differs from traditional GenAI applications.
- Explore the architecture of AutoGen and how it orchestrates multiple LLM-powered agents to collaborate effectively.
- Build and customize various types of agents (e.g., UserProxyAgent, AssistantAgent, GroupChatAgent).
- Implement multi-agent workflows that solve real-world problems with code generation, task breakdown, and dynamic decision making.
- Integrate tools like web APIs, databases, and Python functions into your agent ecosystem.
- Use AutoGen Studio for visual development and monitoring of agent interactions.
- Optimize agents for cost, speed, and performance using configuration tuning and role specialization.
- Deploy agentic systems for use cases like coding assistants, research bots, multi-agent chat applications, and automated task runners.
Requirements
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Basic Python Programming – Students should understand Python syntax, functions, and object-oriented programming.
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Familiarity with APIs – Experience working with REST APIs or using third-party libraries will help in integrating LLMs and tools.
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Introductory AI/ML Knowledge – A basic understanding of large language models and how they work (e.g., prompts, tokens).
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Command Line Usage – Comfort using terminal/command-line tools for installing packages and running scripts.
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Python Environment Setup – Ability to set up and manage virtual environments (e.g., venv or conda) and install dependencies via pip.
Features
- Tailored for Beginners: Whether you're new to AI or looking to expand your knowledge, this course breaks down complex topics into understandable segments, ensuring a solid foundation in Generative AI.
- Hands-On Learning Experience: Move beyond theory with practical exercises, including building your own Generative AI chatbot, getting real-world skills and experience. Note that the chatbot section is totally optional, and if you are from a non-technical background, its ok to skip or just watch along.
- Instructor Guidance: The instructor has deep knowledge and experience in AI and Generative AI, offering insights and guidance throughout the course.
Target audiences
- College Students
- Anyone interested by the buzz around Generative AI
- Aspiring AI/Generative AI Enthusiasts
- Business Professionals and Leaders
- Career Seekers in Tech
- Innovative Problem Solvers
Requirements
- A desire to learn...
- Chatbot section (Optional) requires OpenAI credits
