Building Agents Using the Azure AI Foundry Agent Service

Course details

In this course, Ziggy Zulueta—a Microsoft AI MVP—provides a comprehensive introduction to the Azure AI Agent Service. Learn how to create and manage resources, demonstrate the agent features using the Azure AI Foundry Portal, and utilize the Azure AI Agent Python SDK to create and deploy an agent through sample code and hands-on challenges. Plus, work through a project-based set of challenges that allows you to work through the steps of creating your own agent. By the end of the course, you'll have a solid understanding of this powerful tool and how to apply it within real-world scenarios.

Learning objectives
Create agents using the Azure AI Agent Service.
Add knowledge to your Azure AI Agent.
Add action tools to your Azure AI Agent.
Utilize the enterprise features of Azure with your agent.
Explore the agent features using the Azure AI Foundry Portal.
Deploy agents using the Azure AI Agent Service SDK.

Concepts

0. Introduction

01 - Building agents using the Azure AI Foundry Agent Service
02 - Introduction to the course project

1. Getting Started with Azure AI Foundry Agent Service

03 - Azure AI Foundry Agent Service overview
04 - Setting up the environment
05 - Creating your first agent
06 - Tool overview

2. Knowledge Tools

07 - Grounding with Bing Search
08 - File search
09 - Azure AI Search
10 - SharePoint

3. Action Tools

11 - Function calling
12 - Code interpreter
13 - OpenAPI-defined tools
14 - Azure Functions
15 - Logic Apps

4. More Actions with Agent Service

16 - Build multi-agent systems with connected agents
17 - Tracing agents
18 - Content filtering
19 - Deploying with Chainlit

5. Project - Building Your First Agent

20 - Project overview
21 - Challenge - Setting up the environment
22 - Solution - Setting up the environment
23 - Challenge - Setting up the agents
24 - Solution - Setting up the agents
25 - Challenge - Creating the connected agent
26 - Solution - Creating the connected agent
27 - Challenge - Deploying in Chainlit
28 - Solution - Deploying in Chainlit

Conclusion

29 - Learning more