Get introduced to what AI agents are, how they differ from traditional automation and AI systems, and why they matter. You'll explore real-world examples—like customer support bots and travel agents—to understand key components such as memory, tools, and orchestration.

What is an AI Agent?

Know Your Agent

License to Automate

What Makes an Agent Agentic?

The Anatomy of an Agent

The Root Cause

To Agent or Not to Agent?

The Agent Decision Framework

Build vs Buy: Select Your Tool

Foundations of AI Agents

Dive deeper into how AI agents think and act through frameworks like the Thought-Action-Observation (TAO) loop and ReAct prompting. You'll explore how agents interact with tools, environments, and each other, building toward more advanced multi-agent systems.

The Thought-Action-Observation Cycle

Cracking the TAO Code

Data Science Process

No Reservations: TAO in Action

Do Agents Think of Electric Sheep? The ReAct Framework

CalculateGPT

Can you ReAct?

What's in the (Tool) Box?

Options

Extensions

Functions

Data Stores

Organizing the Toolbox

Tool Mastery

From One to Many: Multi-Agent Systems

Single-Agent System

Multi-Agent System 

One vs Many

The manager pattern

Can I Speak to Your Manager?

Agentic Design Patterns & Architectures

Learn how to use and design AI agents with intention and care. This chapter covers essential guardrails, practical best practices, and how to evaluate when agentic solutions are the right fit—empowering you to apply agentic systems ethically and effectively in real-world contexts.

Minimizing Risks with Guardrails

En Garde!

Safety First

Agentic Systems in the Real World

The AI Agent Playbook

Performance Review

Wrap up

Building and Using AI Agents Responsibly

Cheat Sheet

Course Glossary: 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.

Enroll now, and take your first step toward engaging with the next frontier of AI.

The videos contain live transcripts, which can be accessed by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

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.

Enroll now, and take your first step toward engaging with the next frontier of AI.

Introduction to AI for Work

Discover how AI agents work, what sets them apart from chatbots and automation, and how to use them responsibly—no coding required.

Introduction to AI Agents

Learn the fundamentals of AI agents, their components, and real-world use—no coding required.

Nozioni di base sugli agenti AI

**Thought:** "I need to check available tables for tomorrow at 7 PM first."
**Action:** Query reservation system for 6-person capacity.
**Observation:** Three tables available: Table 12 (bar area), Table 5 (quiet corner), Table 8 (upstairs).

**Thought:** "Now I need to check which tables meet the wheelchair access requirement."
**Action:** Check accessibility features for available tables.
**Observation:** Only Table 5 is wheelchair accessible, Table 8 requires stairs, Table 12 has steps.

**Thought:** "Table 5 is accessible AND in the quiet corner - perfect! Let me book it."
**Action:** Reserve Table 5 for 6 people at 7 PM with accessibility note.
**Observation:** Reservation confirmed with confirmation #R-7891.

**Thought:** "Reservation done. Now I need to arrange the birthday cake."
**Action:** Contact partner bakery for birthday cake order.
**Observation:** Chocolate cake with "Happy Birthday Mom" confirmed for 6:30 PM pickup.

**Thought:** "Everything is arranged. Time to confirm all details with the customer."
**Action:** Send comprehensive confirmation message.
**Observation:** Customer receives full details: Table 5 (quiet, accessible), 7 PM tomorrow, cake arranged.

No Reservations: TAO in Action

Introduction to AI Agents

Esercizio pratico interattivo