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Reviews 4,5
100% fundable

AI-103: Develop AI Apps and Agents on Azure

Intelligent software runs on real data, precise logic, and clear architecture. You'll learn how to design, build, and reliably operate AI applications and agents on Azure.

Key topics

  • Developing generative AI apps and agents with Microsoft Foundry and Azure OpenAI
  • Using Azure Cognitive Services for language, vision, and document processing
  • Planning, configuring, and testing autonomous AI agents
  • Orchestrating and integrating multi-agent architectures
  • Connecting data, tuning models, and bringing solutions into production

Prerequisite
If you have experience in software development or cloud environments, you'll hit the ground running. A basic understanding of AI concepts is helpful but not required.

Target group
Anyone who wants to develop AI applications professionally – whether as a developer, solution architect, or technical specialist with an interest in AI integration.

You'll leave the course with concrete knowledge: how intelligent apps are built, what agents need, and how to take your own AI solutions on Azure from idea to production.

Course Content
  • Key concepts of AI and its role in modern applications.
  • Structure and purpose of Microsoft Foundry.
  • Available Foundry Tools at a glance.
  • Developer tools and SDKs for building AI solutions.
  • Principles of responsible AI in practice.
  • Navigate and search the model catalog.
  • Apply benchmarks to find the right model for your use case.
  • Deploy models to devices and endpoints.
  • Measure and interpret model performance.
  • Try out the model playground to explore capabilities.
  • Choose an appropriate endpoint and SDK.
  • Generate responses with the Responses API.
  • Generate responses with the ChatCompletions API.
  • Understand the concept and purpose of AI tools.
  • Work with the Code Interpreter tool.
  • Apply the Web Search tool in your app.
  • Use the File Search tool to access documents.
  • Build custom logic with the Functions tool.
  • Sharpen model outputs through targeted prompt engineering.
  • Apply Retrieval Augmented Generation to work with real-world data.
  • Fine-tune models to achieve consistent, predictable results.
  • Compare and combine different optimization strategies.
  • Design a generative AI solution with responsibility in mind.
  • Identify potential harms and risks early.
  • Evaluate the severity and likelihood of identified risks.
  • Apply targeted measures to reduce or eliminate risks.
  • Operate and maintain a responsible generative AI solution.
  • Grasp the role of AI agents and the Foundry Agent Service.
  • Get to know different agent development approaches.
  • Build your first agent directly in Microsoft Foundry.
  • Configure Visual Studio Code for agent development.
  • Manage and adjust agents within Visual Studio Code.
  • Extend what agents can do by adding tools.
  • Test, deploy, and connect agents to your applications.
  • Understand why custom tools add value to agent solutions.
  • Explore the options for implementing custom tools.
  • Connect and integrate custom tools into your agent.
  • Learn how MCP tool discovery works.
  • Connect agent tools through an MCP server and client.
  • Use Azure AI agents together with MCP servers.
  • Understand how RAG works in the context of agents.
  • Get to know Foundry IQ and its capabilities.
  • Set up data sources for knowledge bases.
  • Configure how Foundry IQ retrieves and uses information.
  • Learn the available publishing options for Foundry agents.
  • Publish an agent from the Foundry portal to Microsoft Teams.
  • Use the Microsoft 365 Agents Toolkit for advanced scenarios.
  • Access Microsoft 365 data using Work IQ.
  • Test the deployed agent and refine its behavior.
  • Understand what workflows are and how they work.
  • Identify common workflow patterns and when to use them.
  • Create workflows inside Microsoft Foundry.
  • Add agents as active participants in a workflow.
  • Apply Power Fx expressions within workflows.
  • Keep workflows organized and maintainable.
  • Trigger and use workflows programmatically in code.
  • Understand how AI agents are structured in the Microsoft Agent Framework.
  • Build an Azure AI agent using the framework.
  • Extend the agent by adding tools.
  • Get to know the Microsoft Agent Framework and its design principles.
  • Understand how agent orchestration works.
  • Use parallel orchestration for independent tasks.
  • Use sequential orchestration for ordered task chains.
  • Use group chat orchestration for collaborative agents.
  • Apply handoff orchestration to pass control between agents.
  • Use Magentic orchestration for flexible, emergent coordination.
  • Define and describe your A2A agent.
  • Build the agent executor to handle incoming tasks.
  • Host the A2A server and make it reachable.
  • Connect to the A2A agent from a client.
  • Use Azure Language services within Microsoft Foundry Tools.
  • Identify the language of a given text.
  • Extract named entities from unstructured content.
  • Detect and handle personally identifiable information (PII).
  • Explore the features and configuration of the Azure Language MCP server.
  • Connect the Language MCP server to an agent and put it to work.
  • Choose a model that supports speech and audio processing.
  • Convert spoken input to text using speech-to-text.
  • Generate natural-sounding audio output with text-to-speech.
  • Overview of Azure Speech integration within Microsoft Foundry Tools.
  • Apply the Speech-to-Text API to capture spoken input.
  • Work with the Text-to-Speech API to produce audio output.
  • Set up audio formats and choose the right voice.
  • Apply SSML to control speech synthesis in detail.
  • Learn how the Azure Speech MCP server is structured and what it provides.
  • Connect the Speech MCP server to an agent and use it in practice.
  • Discover the features and structure of the Azure Voice Live API.
  • Work through the AI Voice Live Client Library for Python.
  • Build a fully functional Voice Live agent from scratch.
  • Overview of translation capabilities available in Microsoft Foundry.
  • Apply translation to written content.
  • Use speech translation to handle spoken language.
  • Select and configure a vision-capable model in the Microsoft Foundry portal.
  • Build a chat application that processes and responds to image inputs.
  • Understand what image generation models are and how they work.
  • Browse and compare available models in the Microsoft Foundry portal.
  • Build a client application that generates images using a selected model.
  • Deploy and configure a video generation model in Foundry.
  • Create a video from a text prompt.
  • Build a Python script to automate video generation.
  • Learn what content understanding means in the context of Azure AI.
  • Apply content understanding techniques to extract insights from image data.
  • Understand the purpose and scope of Azure Content Understanding.
  • Set up and configure a Content Understanding Analyzer.
  • Apply the Content Understanding API to process multimodal data.
  • Prepare the environment and required permissions for the Content Understanding API.
  • Build and configure a Content Understanding Analyzer.
  • Run analyses on real content and interpret the results.
  • Understand what Azure Document Intelligence does and where it is used.
  • Navigate and use the Document Intelligence Studio.
  • Work with ready-made pre-built models.
  • Train and deploy custom models for specific document types.
  • Understand what Azure AI Search is and how it works.
  • Use an indexer to pull and extract data from sources.
  • Enrich the extracted data with AI-powered skills.
  • Query the index to surface relevant results.
  • Persist extracted information in a knowledge store.

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.

Frequently Asked Questions

  • When processes run automatically, apps become smarter, and development gets faster – efficiency grows and projects scale with less effort.
  • Organisations are turning to autonomous systems to cut costs and let data drive decisions without constant manual input.
  • You learn to design, build, and integrate AI applications, and apply current Azure services to real project scenarios.
  • Especially relevant for anyone in development, data, cloud, or business roles who wants to digitize workflows and extend them with intelligence.
  • Without the right knowledge, teams miss innovation opportunities, struggle with inefficient processes, and fall behind competitors who are already using AI effectively.
  • A structured approach often leads to the first working solutions within a surprisingly short development cycle.
  • Cloud-native services, machine learning, APIs, and agent-based logic – all designed to support scalable and adaptable applications.
  • Deep integration, a wide range of services, and direct connectivity to existing cloud environments make Azure a fast and practical foundation for AI projects.

Your Personal Path to the Right Continuing Education Program—We Provide Personalized Guidance

Our experienced educational advisors will guide you from your very first question all the way to the start of your continuing education program.