AI Agent Fundamentals

Large Language Models (LLMs) and Generative AI have unlocked many valuable use cases, such as content creation and document summarization. For more complex applications like workflow automation, coding assistance, and advanced customer support chatbots, we need a way for LLMs to make decisions and trigger real-world actions: enter AI agents!In this skill track, you'll learn what AI agents are and what they can (and can't!) be used for through real-world examples.You'll discover practical frameworks like the Thought-Action-Observation (TAO) loop and ReAct prompting to better conceptualize how AI agents make decisions.You'll find out what makes an AI agent successful in production, and why so many fail to get out of development. You'll learn the latest practices for building scalable agentic systems, like emerging multi-agent design patterns, the Model Context Protocol (MCP), and the Agent-to-Agent (A2A) protocol.Discover how AI agents can begin changing the way you work today!

1. Introduction to AI for Work

Artificial Intelligence, or AI, has become the biggest technological innovation since the internet, giving everyone access to smart assistants that can assist, augment, or automate our tasks and work. But what is AI, how can you use it to maximize productivity, and how will AI change your job? We'll answer all of these questions in this course through hands-on learning, real-world examples, and interactive exercises that allow you to use real AI chatbot applications. Join the AI revolution today!

2. Introduction to AI Agents

Unlock the power of AI agents and transform how you work with agentic systems. This beginner-friendly course is designed for anyone eager to understand the core concepts behind agentic AI systems, without writing a single line of code.

You'll learn how AI agents differ from traditional automation tools and chatbots, explore their key components like memory, tool use, and orchestration, and discover when and how to implement agent-based solutions for real-world problems. From customer support agents to coding assistants, this course uses relatable examples to demystify what makes AI systems agentic, how they reason, and how to use and build AI agents responsibly.

3. Building Scalable Agentic Systems

Why do so many agentic systems fail to traverse the gap between development and production? In this course, you'll find out! Learn how to design and develop AI agents with scalability in mind, following the three pillars of agentic scalability: robust infrastructure and tooling, modular design architecture, and continuous evaluation and feedback loops. You'll learn about how agentic frameworks like the Model Context Protocol (MCP) and the Agent-to-Agent protocol (A2A) enable cleaner and faster integrations between data sources and agents. Finally, learn about the tests and deployment strategies to set your agent up to deliver value.

Bonus: AI Agents Cheat Sheet

Learn the basics of AI agents with this beginner-friendly cheat sheet, and explore resources to help you get started.