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AI-3026: Develop AI agents on Azure
Build intelligent AI agents that can reason, use tools and work together. This practical, self-paced course shows developers how to create agents with Microsoft Foundry Agent Service, connect custom and MCP tools, design multi-agent solutions and build agents with Semantic Kernel. It’s ideal if you have programming experience and know basic Azure AI concepts.
Duration
8 Hours
Modules
11 Modules
Course type
Microsoft
Course Overview
This intermediate-level learning path introduces AI engineers to the development of intelligent agents using Azure AI Foundry Agent Service and Semantic Kernel. Learners will explore agent architecture, tool integration, multi-agent orchestration, and secure communication protocols. The course prepares participants to build scalable, collaborative AI agents on Azure.
Before You Begin
- Familiarity with Azure AI services
- Understanding of basic AI and ML concepts
- Experience with programming and APIs
- Completion of “Get started with AI” learning path recommended
Key outcomes
- Develop AI Agents Create and deploy intelligent agents on Azure platform.
- Extend with Custom Tools Use MCP and custom tools to enhance agent capabilities.
- Build Multi-Agent Systems Leverage Semantic Kernel for agent collaboration and orchestration.
- Secure & Monitor Agents Enable discovery, secure workflows, and monitor performance.
Tools you’ll use
Azure Portal
Azure AI Studio
Semantic Kernel SDK
Skills you’ll gain
- Intelligent Agent Creation
- Azure Foundry Integration
- Custom Tool Orchestration
- MCP Tool Integration
- Multi-Agent Collaboration
- Secure Agent Communication
- Semantic Kernel Workflows
- Agent Workflow Automation
What You’ll Learn
11 modules
1Module 1: Get started with AI agent development on Azure
1.1.1 Introduction
1.2 What are AI agents?
1.3 Options for agent development
1.4 Microsoft Foundry Agent Service
1.5 Summary
1.2 What are AI agents?
1.3 Options for agent development
1.4 Microsoft Foundry Agent Service
1.5 Summary
2Module 2: Develop an AI agent with Microsoft Foundry Agent Service
2.1 Introduction
2.2 What is an AI agent
2.3 How to use Microsoft Foundry Agent Service
2.4 Develop agents with the Microsoft Foundry Agent Service
2.5 Summary
2.2 What is an AI agent
2.3 How to use Microsoft Foundry Agent Service
2.4 Develop agents with the Microsoft Foundry Agent Service
2.5 Summary
3Module 3: Develop AI agents with the Microsoft Foundry extension in Visual Studio Code
3.1 Introduction
3.2 Get started with the Microsoft Foundry extension
3.3 Part 1 Develop AI agents in Visual Studio Code
3.3 Part 2 Develop AI agents in Visual Studio Code
3.4 Extend AI agent capabilities with tools
3.5 Summary
3.2 Get started with the Microsoft Foundry extension
3.3 Part 1 Develop AI agents in Visual Studio Code
3.3 Part 2 Develop AI agents in Visual Studio Code
3.4 Extend AI agent capabilities with tools
3.5 Summary
4Module 4: Integrate custom tools into your agent
4.1 Introduction
4.2 Why use custom tools
4.3 Options for implementing custom tools
4.4 How to integrate custom tools
4.5 Summary
4.2 Why use custom tools
4.3 Options for implementing custom tools
4.4 How to integrate custom tools
4.5 Summary
5Module 5: Develop a multi-agent solution with Microsoft Foundry Agent Service
5.1 Introduction
5.2 Understand connected agents
5.3 Design a multi-agent solution with connected agents
5.4 Summary
5.2 Understand connected agents
5.3 Design a multi-agent solution with connected agents
5.4 Summary
6Module 6: Integrate MCP Tools with Azure AI Agents
6.1 Introduction
6.2 Understand MCP tool discovery
6.3 Integrate agent tools using an MCP server and client
6.4 Use Azure AI agents with MCP servers
6.5 Summary
6.2 Understand MCP tool discovery
6.3 Integrate agent tools using an MCP server and client
6.4 Use Azure AI agents with MCP servers
6.5 Summary
7Module 7: Develop an AI agent with Microsoft Agent Framework
7.1 Introduction
7.2 Understand Microsoft Agent Framework AI agents
7.3 Create an Azure AI agent with Microsoft Agent Framework
7.4 Add tools to Azure AI agent
7.5 Summary
7.2 Understand Microsoft Agent Framework AI agents
7.3 Create an Azure AI agent with Microsoft Agent Framework
7.4 Add tools to Azure AI agent
7.5 Summary
8Module 8: Orchestrate a multi-agent solution using the Microsoft Agent Framework
8.1 Introduction
8.2 Understand the Microsoft Agent Framework
8.3 Understand agent orchestration
8.4 Use concurrent orchestration
8.5 Use sequential orchestration
8.6 Use group chat orchestration
8.7 Use handoff orchestration
8.8 Use Magentic orchestration
8.9 Summary
8.2 Understand the Microsoft Agent Framework
8.3 Understand agent orchestration
8.4 Use concurrent orchestration
8.5 Use sequential orchestration
8.6 Use group chat orchestration
8.7 Use handoff orchestration
8.8 Use Magentic orchestration
8.9 Summary
9Module 9: Discover Azure AI Agents with A2A
9.1 Introduction
9.2 Define an A2A agent
9.3 Implement an agent executor
9.4 Host an A2A server
9.5 Connect to your A2A agent
9.6 Summary
9.2 Define an A2A agent
9.3 Implement an agent executor
9.4 Host an A2A server
9.5 Connect to your A2A agent
9.6 Summary
10Module 10: Build agent-driven workflows using Microsoft Foundry
10.1 Introduction
10.2 Understand Workflows
10.3 Identify Workflow Patterns
10.4 Create workflows in Microsoft Foundry
10.5 Add Agents to a Workflow
10.6 Apply Power Fx in Workflows
10.7 Maintain Workflows in Microsoft Foundry
10.8 Use workflows in code
10.9 Summary
10.2 Understand Workflows
10.3 Identify Workflow Patterns
10.4 Create workflows in Microsoft Foundry
10.5 Add Agents to a Workflow
10.6 Apply Power Fx in Workflows
10.7 Maintain Workflows in Microsoft Foundry
10.8 Use workflows in code
10.9 Summary
11Module 11: Build knowledge-enhanced AI agents with Foundry IQ
11.1 Introduction
11.2 Understanding RAG for agents
11.3 Explore Foundry IQ
11.4 Part 1 Configure data sources for knowledge bases
11.4 Part 2 Configure data sources for knowledge bases
11.5 Configure retrieval with Foundry IQ
11.6 Summary
11.2 Understanding RAG for agents
11.3 Explore Foundry IQ
11.4 Part 1 Configure data sources for knowledge bases
11.4 Part 2 Configure data sources for knowledge bases
11.5 Configure retrieval with Foundry IQ
11.6 Summary
Certification & Credentials
Certificate track
8 Hours
Target Audience
Intended roles
- Generative AI Engineer
Frequently asked questions
Is this course suitable for beginners in AI?
No, it’s designed for those with foundational AI knowledge.
Do I need to know Semantic Kernel?
No prior experience is required; it’s taught in the course.
Can I build multi-agent systems?
Yes, multi-agent orchestration is a key focus.
Are hands-on labs included?
Yes, each module includes practical exercises.
Is certification available?
No formal certification, but XP is earned on Microsoft Learn.
| Duration | 8 Hours |
|---|---|
| Modules | 11 Modules |
| Course type | Microsoft |
| Category | Microsoft |
| Intended roles | Generative AI Engineer |
| Prerequisites | Familiarity with Azure AI services, Understanding of basic AI and ML concepts, Experience with programming and APIs, Completion of “Get started with AI” learning path recommended |
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