AI-103: Develop AI Apps and Agents on Azure

Price
Net €
VAT €

Price
Price on Request

Duration
4 days

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
Online Live

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The next step in software development is intelligent, adaptive, and context-aware. AI-powered apps and agents open up new possibilities for efficiency and innovation.

Key topics:

Building modern AI applications in Azure.
Using Azure OpenAI and Cognitive Services.
Developing autonomous agents.
Data integration and model management.
Best practices for deployment and monitoring.

Prerequisites
Solid knowledge of development or cloud environments; understanding of basic AI concepts is helpful

Target audience
Developers, solution architects, and technical roles interested in AI integration

Practical understanding of technologies that make digital products smarter, faster, and more competitive.

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Course Content

Plan and Prepare AI Solutions on Azure

What is AI?
Microsoft Foundry
Foundry Tools
Developer tools and SDKs
Responsible AI

Select, deploy, and evaluate Microsoft Foundry models

Browse the model catalog
Use benchmarks to select a model
Equip devices with models
Evaluate model performance

Build a generative AI chat app with Microsoft Foundry

Experiment with the model playground.
Select an endpoint and an SDK.
Generate responses using the Responses API.
Generate responses using the ChatCompletions API.

Develop generative AI apps using tools

What are tools?
Using the Code Interpreter Tool
Using the Web Search Tool
Using the File Search Tool
Using the Functions Tool

Optimize the performance of generative AI models with Microsoft Foundry

Optimize model performance through prompt engineering.
Train models on real data using retrieval-augmented generation.
Fine-tune models for consistent behavior.
Compare and combine optimization strategies.

Implementation of a responsible solution for generative AI in Microsoft Foundry

Planning a responsible approach to generative AI.
Identifying potential risks.
Assessing potential risks.
Mitigating potential risks.
Managing a responsible solution for generative AI.

Develop AI agents using Microsoft Foundry and Visual Studio Code

Understanding AI agents and the Microsoft Foundry Agent Service.
Explore development approaches.
Create your first agent in Microsoft Foundry.
Set up Visual Studio Code for agent development.
Configure and manage agents in Visual Studio Code.
Extend agent functionality with tools.
Test, deploy, and integrate agents.

Integrate custom tools into the agents

Why use custom tools?
Ways to implement custom tools.
Integration of custom tools.

Integrate MCP Tools with Azure AI Agents

Basics of MCP Tool Detection.
Integrate agent tools using an MCP server and client.
Using Azure AI agents with MCP servers.

Building Knowledge-Based AI Agents with Foundry IQ

Understanding RAG for Agents.
Discover Foundry IQ
Configure data sources for knowledge databases.
Configure information gathering with Foundry IQ.

Integrate Agent into Microsoft 365

Understand the publishing options for Foundry agents.
Publish an agent from the Foundry portal to Teams.
Advanced – Use the Microsoft 365 Agents Toolkit.
Access Microsoft 365 data with Work IQ.
Test and optimize the integrated agent.

Create agent-driven workflows with Microsoft Foundry

Understanding workflows.
Recognize workflow patterns.
Create workflows in Microsoft Foundry.
Add agents to a workflow.
Apply Power Fx in workflows.
Manage workflows in Microsoft Foundry.
Use workflows in code.

Develop an AI agent using the Microsoft Agent Framework

Understanding AI agents in the Microsoft Agent Framework.
Create an Azure AI agent using the Microsoft Agent Framework.
Add tools to the Azure AI agent.

Orchestrate a multi-agent solution using the Microsoft Agent Framework

Understanding the Microsoft Agent Framework.
Understanding agent orchestration.
Use parallel orchestration.
Use sequential orchestration.
Use group chat orchestration.
Using handoff orchestration.
Use Magentic orchestration.

Get started with Azure AI Agents using A2A

Define an A2A agent
Implement the agent executor.
Host the A2A server
Establish a connection to the A2A agent.

Text Analysis with Azure Language in Foundry Tools

Azure Language in Microsoft Foundry Tools.
Detect language
Extract entities
Extract personally identifiable information (PII).

Develop a text analysis agent using the Azure Language MCP server

Get to know the Azure Language MCP Server.
Connect to the Language MCP server and use it with an agent.

Develop a language-capable generative AI application

Select a language-capable model.
Transcribe speech
Synthesize speech

Create voice-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 formats and voices.
Use the Speech Synthesis Markup Language.

Develop a voice agent using Azure Speech MCP Server

Understanding the Azure Speech MCP Server.
Connect to and use the Speech MCP server with an agent.

Development of an Azure Speech Live Agent

Exploration of the Azure Voice Live API.
Investigation of the AI Voice Live Client Library for Python.
Development of a Voice Live agent.

Translating Text and Speech with Microsoft Foundry Tools

Translation in Microsoft Foundry
Translate text
Translate language

Develop a generative AI application with image processing capabilities

A model capable of image processing in the Microsoft.
Foundry portal.
Develop an image-based chat application.

Generate images using AI

What are image generation models?
Explore models in the Foundry portal
Create a client app with an image model

Create videos with Microsoft Foundry

Provide a video generation model.
Generate a video from a command prompt.
Generate video in Python.

Analyze images using content understanding

What is content understanding?
Evaluate images using content understanding.

Build a multimodal analytics solution with Azure Content Understanding

What is Azure Content Understanding?
Create a Content Understanding Analyzer.
Use the Content Understanding API.

Create an Azure Content Understanding client application

Preparing to use the AI Content Understanding API.
Creating a Content Understanding Analyzer.
Analyzing content.

Extract data with Azure Document Intelligence

What is Azure Document Intelligence?
Using Document Intelligence Studio.
Using pre-built models.
Train and use custom models.

Build a knowledge mining solution with Azure AI Search

What is Azure AI Search?
Extract data using an indexer.
Enrich extracted data with AI capabilities.
Search an index.
Store extracted information in a knowledge store.

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently Asked Questions

What specific benefits does knowledge of Azure AI Apps and Agents offer in the workplace?

Automated processes, intelligent applications, and faster development boost efficiency and make projects scalable.

Why is the development of AI agents in such high demand right now?

Companies are increasingly turning to autonomous systems to reduce costs and automate data-driven decision-making.

What practical skills are developed when working with Azure AI solutions?

Design, development, and integration of AI apps, as well as the use of modern Azure services for real-world use cases.

For whom is a focus on AI-powered applications particularly worthwhile?

Relevant for roles in development, data, cloud, and business that digitize and intelligently enhance processes.

What risks arise without expertise in Azure AI?

Missed opportunities for innovation, inefficient processes, and increasing competitive pressure from better-positioned teams.

How quickly can you put your own AI apps into production?

With a structured approach, the first functional solutions often emerge after only a short development period.

What technologies are at the heart of modern AI solutions on Azure?

Cloud-native services, machine learning, APIs, and agent logic for scalable and flexible applications.

Why is Azure a key platform for AI projects?

A high level of integration, a wide range of services, and direct connectivity to existing cloud infrastructures enable rapid implementation.

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