Course Overview
Course Description
Unlock the future of intelligent applications by learning to develop AI agents on Azure. In this one-day, intermediate-level course, AI engineers and developers will design, build, and manage sophisticated AI agents using Azure AI Agent Service and Semantic Kernel. Dive into modular workflows—starting with foundational agent design, advancing through tool integration, and expanding to multi-agent orchestration. Learn to deploy scalable, context-aware solutions that automate complex workflows. Approximately 40–50% of class time is dedicated to live coding and real-world implementations.
Target Audience
Perfect for:
AI Engineers, Software Developers, and Data Scientists building AI-powered agent solutions in Azure
Professionals with basic programming and cloud fundamentals, familiar with REST/JSON services
Prerequisites:
General understanding of AI and Azure fundamentals
Programming experience (e.g., Python, C#)
Course Outline
Module1: Get Started with AI Agent Development on Azure
Understand what AI agents are and when to use them
Explore development options using Azure AI Agent Service
Module2: Develop an AI Agent Using Azure AI Agent Service
Create and configure agents using the Azure AI Agent Service environment
Learn deployment patterns and management workflows
Module3: Integrate Custom Tools into Your AI Agent
Extend agent capabilities through custom tool integration
Implement richer, context-aware functionality in your agents
Module4: Build AI Agents with Semantic Kernel
Develop intelligent agents using the Semantic Kernel SDK
Add plugins and adjust agent behaviors programmatically
Module5: Orchestrate Multi-Agent Solutions with Semantic Kernel
Implement multi-agent orchestration using Semantic Kernel Agent Framework
Design agent collaboration strategies, selection logic, and termination flows
HandsOn Experience
Expect roughly 40–50% of the course to be hands-on, featuring guided coding, demos, and live implementation of AI agent development on Azure using real-world scenarios.
Skills You’ll Gain
By completing AI3026, you will be able to:
Distinguish between AI agent types and use-case suitability
Build, deploy, and manage agents via the Azure AI Agent Service
Integrate custom tools to enhance agent functionality
Develop agent logic and plugins using Semantic Kernel
Orchestrate collaborative multi-agent systems for intelligent workflows
🤖 AI Agent-Led Learning Experience
Intelligent Learning Support
AI agents provide 24/7 personalized and expert instruction, adapting to your learning pace and style.
Personalized Practice
Receive customized exercises and scenarios based on your progress and areas for improvement.
Continuous Assessment
Real-time feedback and progress tracking help you stay on track and achieve your learning goals.
Hands-On Labs
This course includes practical, hands-on laboratory exercises to reinforce your learning:
Lesson 1: Introduction to AI Agents and Azure AI Foundry
Unlock the full potential of generative AI with Microsoft’s Azure AI Studio. In this introductory lesson, you’ll explore the Azure AI Studio and Foundry Portal—your central workspace for building, managing, and deploying generative AI applications.
You’ll learn how to create AI projects, connect data sources, and provision the tools needed to build intelligent solutions using powerful foundation models. This lesson sets the stage for everything to come by giving you hands-on experience with the platform and guiding you through best practices for managing AI resources in Azure.
By the end of this lesson, you’ll understand how Azure AI Studio fits into the generative AI development workflow—and you’ll be ready to start building your first AI-powered project.
Lesson 2: Build an AI Agent with Code Interpreter
In this lesson, you’ll take your AI agent skills to the next level by enabling your agent to execute live Python code using Azure AI Studio’s built-in Code Interpreter tool.
You’ll learn how to build an intelligent agent that can analyze data, generate charts, perform calculations, and return downloadable results—transforming your agent into a true computational assistant. Whether it's summarizing business metrics or visualizing trends, this capability is a game-changer for automating data-driven tasks.
Through a hands-on lab, you’ll configure the agent, connect it with a Python client, upload datasets, and interact with it through dynamic prompts. By the end of this lesson, you’ll know how to empower agents to deliver real-time insights and visualizations—all powered by code execution in a secure, sandboxed environment.
Lesson 3: Extend Agents with Custom Functions
In this lesson, you’ll learn how to take AI agents beyond conversation and into action. Using custom functions, you’ll enable agents to perform real-world tasks like submitting support tickets, retrieving data, or triggering operations—based on natural language prompts from users.
You’ll define Python functions, register them as tools, and integrate them into an agent’s toolkit using Azure AI Foundry. Then, you’ll see how the agent intelligently decides when to call these functions based on user intent.
Through a hands-on lab, you’ll build a support assistant that collects problem details and creates a simulated support ticket. This lesson demonstrates how to give your agents the power to act, not just respond—unlocking powerful automation potential across industries.
Lesson 4: Use Semantic Kernel to Build Agents
This lesson introduces you to Semantic Kernel (SK)—Microsoft’s open-source SDK for building code-first, intelligent AI agents. Unlike UI-driven tools, Semantic Kernel offers full control and flexibility through plugins, making it ideal for developers who want to create reusable, modular AI workflows.
You’ll learn how to define plugins, manage conversational threads, and build agents that can perform structured tasks like sending emails or integrating with external systems. With support for both Python and C#, SK provides deep customization while still leveraging the power of large language models.
Through a hands-on lab, you’ll build an expense submission agent that uses a plugin to simulate sending a claim email. This lesson shows how SK helps you scale AI agent development with clean architecture and real-world integrations.
Lesson 5: Orchestrate Multi-Agent Solutions
In this final lesson, you’ll learn how to design and orchestrate multi-agent AI systems using Microsoft’s Semantic Kernel SDK. Instead of relying on a single agent to handle every task, you’ll build a team of specialized agents—each with distinct roles, tools, and responsibilities—that work together to solve complex problems.
You’ll explore real-world coordination strategies such as how agents take turns, share context, and determine when a task is complete. Through hands-on practice, you’ll create an automated incident resolution system featuring a log analysis agent and a DevOps remediation agent, working together in a coordinated conversation.
By the end of this lesson, you’ll be able to build scalable, role-based AI solutions that reflect how real teams operate—collaborating to achieve outcomes more efficiently than a single model can on its own.
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