Develop AI apps and agents on Azure

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

Select, deploy, and evaluate Microsoft Foundry models

Explore how to select appropriate models from the model catalog using benchmarks, deploy them to endpoints, and evaluate their performance using manual and automated approaches in Microsoft Foundry portal.

• Explore the model catalog
• Select models using benchmarks
• Deploy models to endpoints
• Evaluate model performance
• Exercise - Select, deploy, and evaluate models

Develop a generative AI chat app with Microsoft Foundry

Use Microsoft Foundry to develop generative AI chat applications with projects and the Responses API.

• Explore with the model playground
• Choose an endpoint and SDK
• Generate responses with the Responses API
• Generate responses with the ChatCompletions API
• Exercise - Create a generative AI chat app

Develop generative AI apps that use tools

Tools enable models to perform tasks and interact with external systems, enabling them to extend their capabilities beyond basic chat interactions.

• What are tools?
• Use the code_interpreter tool
• Use the web_search tool
• Use the file_search tool
• Use the function tool
• Exercise - Create a generative AI chat app that uses tools
• Module assessment

Optimize generative AI model performance with Microsoft Foundry

Explore complementary strategies to optimize generative AI model performance, including prompt engineering, system messages, model parameters, Retrieval Augmented Generation (RAG), and fine-tuning. Learn when to use each strategy and how to combine them.

• Optimize model output with prompt engineering
• Ground your model with Retrieval Augmented Generation
• Fine-tune a model for consistent behavior
• Compare and combine optimization strategies
• Exercise - Optimize generative AI model performance
• Module assessment

Implement a responsible generative AI solution in Microsoft Foundry

A practical approach to implementing generative AI responsibly, and exploring guardrails in AI Foundry portal.

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

Develop AI agents with Microsoft Foundry and Visual Studio Code

Learn how to build, test, and deploy AI agents using Microsoft Foundry Agent Service through both the Azure portal and Visual Studio Code extension.

• Understand AI agents and Microsoft Foundry Agent Service
• Explore development approaches
• Build your first agent in Microsoft Foundry
• Set up Visual Studio Code for agent development
• Configure and manage agents in Visual Studio Code
• Extend agent capabilities with tools
• Test, deploy, and integrate agents
• Exercise - Build and deploy an AI agent

Integrate custom tools into your agent

Learn how to build an agent with custom tools using the Microsoft Foundry Agent Service.

• Why use custom tools
• Options for implementing custom tools
• How to integrate custom tools
• Exercise - Build an agent with custom tools
• Module assessment

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
• Module assessment

Build knowledge-enhanced AI agents with Foundry IQ

Learn how Foundry IQ transforms how AI agents access and reason over organizational data through its unified knowledge layer and Retrieval Augmented Generation (RAG) capabilities. Build a practical agent that leverages enterprise knowledge through Foundry IQ's agentic retrieval engine.

• 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

Integrate your agent with Microsoft 365

Learn how to publish Microsoft Foundry agents to Microsoft Teams and Microsoft 365 Copilot, access workplace data with Work IQ, and test your integrated agents.

• Understand Foundry agent publishing options
• Publish an agent from Foundry portal to Teams
• Advanced - Use Microsoft 365 Agents Toolkit
• Access Microsoft 365 data with Work IQ
• Test and iterate your integrated agent
• Exercise - Publish a Foundry agent to Teams

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
• Module Assessment

Develop an AI agent with Microsoft Agent Framework

Learn how to start building 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 design multi-agent solutions with the Microsoft Agent Framework SDK

• Understand the Microsoft Agent Framework
• Understand agent orchestration
• Use concurrent orchestration
• Use sequential orchestration
• Use group chat orchestration
• Use handoff orchestration
• Use Magentic orchestration
• Exercise - Develop a multi-agent solution

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
• Module assessment

Analyze text with Azure Language in Foundry Tools

Use Azure Language in Foundry Tools to built text analysis solutions.

• Azure Language in Microsoft Foundry Tools
• Detect language
• Extract entities
• Extract personally identifiable information (PII)
• Exercise - Analyze text
• Module assessment

Develop a text analysis agent with the Azure Language MCP server

Learn how to build an AI agent that uses the Azure Language MCP server to perform text analysis tasks like language detection, entity recognition, and personal information redaction.

• Understand the Azure Language MCP server
• Connect and use the Language MCP server with an agent
• Exercise - Develop a text analysis agent

Develop a speech-capable generative AI application

A voice carries meaning beyond words. Learn how to use models that transcribe and synthesize speech.

• Choose a speech-capable model
• Transcribe speech
• Synthesize speech
• Exercise - Use speech-capable generative AI models
• Module assessment

Create speech-enabled apps with Azure Speech in Microsoft Foundry Tools

Create speech-enabled apps with Azure Speech in Microsoft Foundry Tools.

• Azure Speech in Foundry Tools
• Use the Speech to Text API
• Use the Text to Speech API
• Configure audio format and voices
• Use Speech Synthesis Markup Language
• Exercise - Create a speech-enabled app
• Module assessment

Develop a speech agent with the Azure Speech MCP server

Learn how to build an AI agent that uses the Azure Speech MCP server to perform speech-to-text and text-to-speech tasks.

• Understand the Azure Speech MCP server
• Connect and use the Speech MCP server with an agent
• Exercise - Use Azure Speech in an agent

Develop an Azure Speech Voice Live Agent in Microsoft Foundry

Learn how to develop an Azure Speech Voice Live Agent using the Voice Live API and SDK in Microsoft Foundry.

• Explore the Azure Voice Live API
• Explore the AI Voice Live client library for Python
• Create a Voice Live agent
• Exercise - Develop a Voice Live agent
• Module assessment

Translate text and speech with Microsoft Foundry Tools

Translate text and speech using Azure Translator and Azure Speech in Foundry Tools

• Translation in Microsoft Foundry
• Translate text
• Translate speech
• Exercise - Translate text and speech
• Module assessment

Develop a vision-enabled generative AI application

A picture says a thousand words, and multimodal generative AI models can interpret images to respond to visual prompts. Learn how to build vision-enabled chat apps.

• Use a vision-capable model in the Microsoft Foundry portal
• Develop a vision-based chat app
• Exercise - Develop a vision-enabled chat app
• Module assessment

Generate images with AI

Use models in Microsoft Foundry to generate images

• What are image-generation models?
• Explore image-generation models in Microsoft Foundry portal
• Create a client application that uses an image generation model
• Exercise - Generate images with AI
• Module assessment

Generate videos with Microsoft Foundry

Learn how to generate videos from text prompts with Sora 2 in Microsoft Foundry.

• Deploy a video generating model
• Generate video from a prompt
• Generate video in Python
• Exercise - Generate video with Sora 2 in Microsoft Foundry
• Module assessment

Analyze images with Content Understanding

Learn how to analyze images with Azure Content Understanding.

• What is Content Understanding?
• Analyze images with Content Understanding
• Exercise - Analyze images with Content Understanding
• Module assessment

Create a multimodal analysis solution with Azure Content Understanding

Use Azure Content Understanding for multimodal content analysis and information extraction.

• What is Azure Content Understanding?
• Create a Content Understanding analyzer
• Use the Content Understanding API
• Exercise - Extract information from multimodal content
• Module assessment

Create an Azure Content Understanding client application

Use the Azure Content Understanding API for multimodal content analysis and information extraction.

• Prepare to use the AI Content Understanding API
• Create a Content Understanding analyzer
• Analyze content
• Exercise - Develop a Content Understanding client application
• Module assessment

Extract data with Azure Document Intelligence

Azure Document Intelligence uses OCR and deep learning to extract text, key-value pairs, tables, and structured data from documents. Learn how to use prebuilt models, train custom models, and build document processing solutions.

• What is Azure Document Intelligence?
• Use the Document Intelligence Studio
• Use prebuilt models
• Train and use custom models
• Exercise - Analyze documents with Document Intelligence
• Module assessment

Create a knowledge mining solution with Azure AI Search

Unlock the hidden insights in your data with Azure AI Search.

• What is Azure AI Search?
• Extract data with an indexer
• Enrich extracted data with AI skills
• Search an index
• Persist extracted information in a knowledge store
• Exercise - Create a knowledge mining solution
• Module assessment