Plan and prepare to develop AI solutions on Azure

Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team.

• What is AI?
• Foundry Tools
• Microsoft Foundry
• Developer tools and SDKs
• Responsible AI
• Exercise - Prepare for an AI development project

Choose and deploy models from the model catalog in Microsoft Foundry portal

Choose the various language models that are available through the Microsoft Foundry's model catalog. Understand how to select, deploy, and test a model, and to improve its performance.

• Explore the model catalog
• Deploy a model to an endpoint
• Optimize model performance
• Exercise - Explore, deploy, and chat with language models

Develop an AI app with the Microsoft Foundry SDK

Use the Microsoft Foundry SDK to develop AI applications with Microsoft Foundry projects.

• What is the Microsoft Foundry SDK?
• Work with project connections
• Create a chat client

 

• Exercise - Create a generative AI chat app

Get started with prompt flow to develop language model apps in the Microsoft Foundry

Learn about how to use prompt flow to develop applications that leverage language models in the Microsoft Foundry.

• Understand the development lifecycle of a large language model (LLM) app
• Understand core components and explore flow types
• Explore connections and runtimes
• Explore variants and monitoring options
• Exercise - Get started with prompt flow

Develop a RAG-based solution with your own data using Microsoft Foundry

Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to ground prompts with your data. Microsoft Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.

• Understand how to ground your language model
• Make your data searchable
• Create a RAG-based client application
• Implement RAG in a prompt flow
• Exercise - Create a generative AI app that uses your own data

Fine-tune a language model with Microsoft Foundry

Train a base language model on a chat-completion task. The model catalog in Microsoft Foundry offers many open-source models that can be fine-tuned for your specific model behavior needs.

• Understand when to fine-tune a language model
• Prepare your data to fine-tune a chat completion model
• Explore fine-tuning language models in Microsoft Foundry portal
• Exercise - Fine-tune a language model

Implement a responsible generative AI solution in Microsoft Foundry

Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.

• Plan a responsible generative AI solution
• Map potential harms
• Measure potential harms
• Mitigate potential harms
• Manage a responsible generative AI solution
• Exercise - Apply content filters to prevent the output of harmful content

Evaluate generative AI performance in Microsoft Foundry portal

Evaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio.

• Assess the model performance
• Manually evaluate the performance of a model
• Automated evaluations
• Exercise - Evaluate generative AI model performance

Get started with AI agent development on Azure

AI agents represent the next generation of intelligent applications. Learn how they can be developed and used on Microsoft Azure.

• What are AI agents?
• Options for agent development
• Microsoft Foundry Agent Service
• Exercise - Explore AI Agent development

Develop an AI agent with Microsoft Foundry Agent Service

This module provides engineers with the skills to begin building agents with Microsoft Foundry Agent Service.

• What is an AI agent
• How to use Microsoft Foundry Agent Service
• Develop agents with the Microsoft Foundry Agent Service
• Exercise - Build an AI agent

Develop AI agents with the Microsoft Foundry extension in Visual Studio Code

Learn how to build, test, and deploy AI agents using the Microsoft Foundry extension in Visual Studio Code.

• Get started with the Microsoft Foundry extension
• Develop AI agents in Visual Studio Code
• Extend AI agent capabilities with tools
• Exercise - Build an AI agent using the Microsoft Foundry extension

Integrate custom tools into your agent

• Built-in tools are useful, but they may not meet all your needs. In this module, learn how to extend the capabilities of your agent by integrating custom tools for your agent to use.
• Why use custom tools
• Options for implementing custom tools
• How to integrate custom tools
• Exercise - Build an agent with custom tools

Develop a multi-agent solution with Microsoft Foundry Agent Service

Break down complex tasks with intelligent collaboration. Learn how to design multi-agent solutions using connected agents.

• Understand connected agents
• Design a multi-agent solution with connected agents
• Exercise - Develop a multi-agent app with Microsoft Foundry

Integrate MCP Tools with Azure AI Agents

Enable dynamic tool access for your Azure AI agents. Learn how to connect MCP-hosted tools and integrate them seamlessly into agent workflows.

• Understand MCP tool discovery
• Integrate agent tools using an MCP server and client
• Use Azure AI agents with MCP servers
• Exercise - Connect MCP tools to Azure AI Agents

Develop an AI agent with Microsoft Agent Framework

This module provides engineers with the skills to begin building Microsoft Foundry Agent Service agents with Microsoft Agent Framework.

• Understand Microsoft Agent Framework AI agents
• Create an Azure AI agent with Microsoft Agent Framework
• Add tools to Azure AI agent
• Exercise - Develop an Azure AI agent with the Microsoft Agent Framework SDK

Orchestrate a multi-agent solution using the Microsoft Agent Framework

Learn how to use the Microsoft Agent Framework SDK to develop your own AI agents that can collaborate for a multi-agent solution.

• Understand the Microsoft Agent Framework
• Understand agent orchestration
• Use concurrent orchestration
• Use sequential orchestration
• Use group chat orchestration
• Use handoff orchestration
• Use Magentic orchestration

Discover Azure AI Agents with A2A

Learn how to implement the A2A protocol to enable agent discovery, direct communication, and coordinated task execution across remote agents.

• Define an A2A agent
• Implement an agent executor
• Host an A2A server
• Connect to your A2A agent
• Exercise - Connect to remote Azure AI Agents with the A2A protocol

Build agent-driven workflows using Microsoft Foundry

Workflows enable you to orchestrate AI agents and other components to create intelligent applications. Learn how to build and manage workflows using Microsoft Foundry.

• Understand Workflows
• Identify Workflow Patterns
• Create workflows in Microsoft Foundry
• Add Agents to a Workflow
• Apply Power Fx in Workflows
• Maintain Workflows in Microsoft Foundry
• Use workflows in code
• Exercise - Create an Agent-driven Workflow

Build knowledge-enhanced AI agents with Foundry IQ

Learn how to connect AI agents with enterprise knowledge using Foundry IQ. You'll explore how Retrieval Augmented Generation (RAG) solves the knowledge problem for AI agents, discover how Foundry IQ provides a shared knowledge platform that multiple agents can access, improve retrieval quality through data optimization, and configure agent instructions for consistent, cited responses.

• Understanding RAG for agents
• Explore Foundry IQ
• Configure data sources for knowledge bases
• Configure retrieval with Foundry IQ
• Exercise - Integrate an AI agent with Foundry IQ