Building AI Agents & Agentic AI System via Microsoft Autogen

Master Microsoft AutoGen to build powerful AI agents, automate tasks, and create advanced Agentic AI systems.

What you'll learn
- Understand the fundamentals of agentic AI and how autonomous agents interact using the Microsoft AutoGen framework.
- Set up and configure AutoGen to build AI systems with multiple collaborative agents.
- Design and implement custom agents that perform tasks like coding, reasoning, and decision-making.
- Build and deploy real-world multi-agent workflows that automate complex tasks end-to-end.

Requirements
- Basic Python Programming – Students should understand Python syntax, functions, and object-oriented programming.
- Familiarity with APIs – Experience working with REST APIs or using third-party libraries will help in integrating LLMs and tools.
- Introductory AI/ML Knowledge – A basic understanding of large language models and how they work (e.g., prompts, tokens).
- Command Line Usage – Comfort using terminal/command-line tools for installing packages and running scripts.
- Python Environment Setup – Ability to set up and manage virtual environments (e.g., venv or conda) and install dependencies via pip.

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.

This course is project-focused—you won’t just learn the theory, you’ll build powerful agentic AI applications from scratch. You’ll understand how to design autonomous AI teams that mirror human workflows, assign responsibilities, communicate efficiently, and adapt to dynamic tasks.

We’ll also compare AutoGen with other orchestration frameworks like LangChain and CrewAI, giving you a well-rounded perspective of what tools to use and when.

Who should take this course?

This course is ideal for:

ML and AI professionals wanting to transition into LLM-powered agentic development.

Developers interested in building intelligent apps that go beyond chatbots.

GenAI enthusiasts eager to push the limits of LLM capabilities using agent collaboration.

Startup founders and product teams working on AI-first applications.

Students and researchers looking to build hands-on projects with cutting-edge agentic frameworks.

By the end of this course, you will have the confidence and skills to build, scale, and deploy AI agent ecosystems that can reason, act, and collaborate just like teams of humans—powered by the latest advancements in AutoGen and Agentic AI.

Who this course is for:
- Aspiring Data Scientists & Analysts – Beginners who want to build a strong foundation in data science, machine learning, and AI concepts.
- Working Professionals – Engineers, software developers, and IT professionals looking to upskill or switch careers into AI, ML, or data-related roles.
- Students & Graduates – College students and recent graduates from computer science, mathematics, statistics, or engineering backgrounds aiming to prepare for industry roles.
- AI Enthusiasts & Researchers – Individuals with a passion for AI who want to explore practical applications of NLP, MLOps, and GenAI technologies.
- Freelancers & Entrepreneurs – Professionals aiming to build AI-powered products, freelance in the ML domain, or start their own ventures using modern open-source tools and deployment strategies.
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