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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Module 16: Develop natural language solutions in Azure
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 17: Extract insights from visual data on Azure
Use generative AI, computer vision, and Content Understanding capabilities in Azure to extract insights from visual data, supporting scenarios like: