AI-103 Develop AI Apps and Agents on Azure

Generative artificial intelligence (AI) is becoming more accessible through comprehensive development platforms like Microsoft Foundry. Learn how to build generative AI applications that use language models to interact with your users.

Module 1: 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.

• Introduction
• What is AI?
• Microsoft Foundry
• Foundry Tools
• Developer tools and SDKs
• Responsible AI
• Exercise – Prepare for an AI development project
• Module assessment
• Summary

Module 2: 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.

• Introduction
• Explore the model catalog
• Select models using benchmarks
• Deploy models to endpoints
• Evaluate model performance
• Exercise – Select, deploy, and evaluate models
• Knowledge check
• Summary

Module 3: Develop a generative AI chat app with Microsoft Foundry

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

• Introduction
• 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
• Knowledge check
• Summary

Module 4: 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.

• Introduction
• What are tools?
• Use the code_interpreter tool
• Use the web_search tool
• Use the file_search tool
• Use the functions tool
• Exercise – Create a generative AI chat app that uses tools
• Module assessment
• Summary

Module 5: Optimize generative AI model performance with Microsoft Foundry

Explore complementary strategies to optimize generative AI model performance. Learn how to apply prompt engineering, ground your model with RAG, and fine-tune for consistent behavior—and when to combine these approaches.

• Introduction
• 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
• Summary

Module 6: 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.

• Introduction
• 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
• Summary

Generative Artificial Intelligence (AI) is becoming more functional and accessible, and AI agents are a key component of this evolution. This learning path will help you understand the AI agents, including when to use them and how to build them, using Microsoft Foundry Agent Service and Microsoft Agent Framework. By the end of this learning path, you will have the skills needed to develop AI agents on Azure.

Module 7: 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.

• Introduction
• 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
• Knowledge checkSummary

Module 8: 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.

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

Module 9: 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.

• Introduction
• 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
• Summary

Module 10: 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.

• Introduction
• 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
• Knowledge check
• Summary

Module 11: 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.

• Introduction
• 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
• Knowledge check
• Summary

Module 12: 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.

• Introduction
• 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
• Summary

Module 13: 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.

• Introduction
• 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
• Knowledge check
• Summary

Module 14: 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.

• Introduction
• 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
• Knowledge check
• Summary

Module 15: 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.

• Introduction
• 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
• Summary

Natural language solutions use language models to interpret the semantic meaning of written or spoken language, and in some cases respond based on that meaning. You can use Microsoft Foundry to develop AI apps and agents that can analyze text, transcribe and synthesize speech, and translate languages.

Module 16: Analyze text with Azure Language in Foundry Tools

Azure Language in Foundry Tools enables you to create intelligent apps and services that extract semantic information from text.

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

Module 17: 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.

• Introduction
• Understand the Azure Language MCP server
• Connect and use the Language MCP server with an agent
• Exercise – Develop a text analysis agent
• Knowledge check
• Summary

Module 18: Develop a speech-capable generative AI application

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

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

Module 19: Create speech-enabled apps with Azure Speech in Microsoft Foundry Tools

Azure Speech in Microsoft Foundry Tools enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.

• Introduction
• 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
• Summary

Module 20: 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.

• Introduction
• Understand the Azure Speech MCP server
• Connect and use the Speech MCP server with an agent
• Exercise – Use Azure Speech in an agent
• Knowledge check
• Summary

Module 21: Develop an Azure Speech Voice Live Agent in Microsoft Foundry

Learn how to develop a Voice Live agent using the Voice Live API and SDK. This module covers the fundamentals of the Voice Live platform, including API integration, SDK usage, and building conversational AI agents.

• Introduction
• 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
• Summary

Module 22: Translate text and speech with Microsoft Foundry Tools

The Translator and Speech services enable you to create intelligent apps and services that can translate text and speech between languages.

• Introduction
• Translation in Microsoft Foundry
• Translate text
• Translate speech
• Exercise – Translate text and speech
• Module assessment
• Summary

Use generative AI, computer vision, and Content Understanding capabilities in Azure to extract insights from visual data, supporting scenarios like:

• Image analysis
• Image and video generation
• Content Understanding and enrichment
• Visual search and classification
• Digital asset management (DAM)
• Multimodal AI solutions

Module 23: 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.

• Introduction
• 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
• Summary

Module 24: Generate images with AI

In Microsoft Foundry, you can use image generation models to create original images based on natural language prompts.

• Introduction
• 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
• Summary

Module 25: Generate videos with Microsoft Foundry

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

• Introduction
• 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
• Summary

Module 26: Analyze images with Content Understanding

Learn how to analyze images with Azure Content Understanding.

• Introduction
• What is Content Understanding?
• Analyze images with Content Understanding
• Exercise – Analyze images with Content Understanding
• Module assessment
• Summary

Module 27: Create a multimodal analysis solution with Azure Content Understanding

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

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

Module 28: Create an Azure Content Understanding client application

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

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

Module 29: Extract data with Azure Document Intelligence

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

• Introduction
• 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
• Summary

Module 30: Create a knowledge mining solution with Azure AI Search

Unlock the hidden insights in your data with Azure AI Search. In this module, you’ll learn how to implement a knowledge mining solution that extracts and enriches data, making it searchable and ready for deeper analysis.

• Introduction
• 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
• Summary