Introduction to AI Agents

No programming is required. This course is entirely conceptual and designed for beginners, knowledge workers, and tech professionals looking to understand AI agent systems without writing code.

AI agents can power a wide range of business applications, from customer support and data analysis to task automation and intelligent assistants. You’ll learn how to identify real-world use cases where agentic systems add the most value.

The course covers key principles of responsible AI agents, including using guardrails, human-in-the-loop design patterns, and best practices for building and using AI agents safely in professional environments.

You’ll explore foundational frameworks like the Thought-Action-Observation (TAO) loop and ReAct prompting, which help explain how AI agents reason, act, and interact with tools and environments.

ReAct (Reasoning Acting) is a prompting framework that enables AI agents to combine thought processes with tool use in a structured loop. It allows agents to reason through problems, take actions (like API calls or tool usage), and adapt their behavior based on the results—making them more dynamic and intelligent.

The Thought-Action-Observation (TAO) loop is a core pattern in agentic systems where an AI agent thinks (analyzes a problem), acts (performs a task or uses a tool), and observes (interprets the result). This cycle allows agents to operate interactively and respond to changing conditions in real time.