Merge pull request #49295 from MicrosoftDocs/NEW-agent-service-modules

New agent service modules

Author: denrea

learn-pr/paths/develop-ai-agents-on-azure/index.yml

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+### YamlMime:LearningPath
+uid: learn.wwl.develop-ai-agent-on-azure
+metadata:
+  title: Develop AI agents on Azure
+  description: Learn how to develop AI agents on Azure using the Azure AI Agent Service and Semantic Kernel Agent Framework. 
+  ms.date: 02/24/2025
+  author: wwlpublish
+  ms.author: berryivor
+  ms.topic: learning-path
+  ms.collection: wwl-ai-copilot
+title: Develop AI agents on Azure
+prerequisites: |
+  Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the [Get started with artificial intelligence](/training/paths/get-started-with-artificial-intelligence-on-azure) learning path first.
+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 Azure AI Agent Service and Semantic Kernel Agent Framework. By the end of this learning path, you will have the skills needed to develop AI agents on Azure.
+iconUrl: /training/achievements/generic-badge.svg
+levels:
+- intermediate
+roles:
+- ai-engineer
+products:
+- ai-services
+subjects:
+- artificial-intelligence
+- machine-learning
+- natural-language-processing
+modules:
+- learn.wwl.ai-agent-fundamentals
+- learn.wwl.develop-ai-agent-azure
+trophy:
+  uid: learn.wwl.develop-ai-agent-on-azure.trophy

learn-pr/wwl-data-ai/develop-ai-agent-azure/1-introduction.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: Learn how to start using Azure AI Agent Service.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)]

learn-pr/wwl-data-ai/develop-ai-agent-azure/2-what-is-ai-agent.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.what-is-ai-agent
+title: What is an AI agent
+metadata:
+  title: What is an AI agent
+  description: This unit explains what an AI agent is and how it works.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 6
+content: |
+  [!include[](includes/2-what-is-ai-agent.md)]

learn-pr/wwl-data-ai/develop-ai-agent-azure/3-how-use-agent-service.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.how-use-agent-service
+title: How to use Azure AI Agent Service
+metadata:
+  title: How to use Azure AI Agent Service
+  description: This unit explores how to use Azure AI Agent Service. 
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 7
+content: |
+  [!include[](includes/3-how-use-agent-service.md)]

learn-pr/wwl-data-ai/develop-ai-agent-azure/4-when-use-agent-service.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.when-use-agent-service
+title: When to use Azure AI Agent Service
+metadata:
+  title: When to use Azure AI Agent Service
+  description: This unit explains when and why to use Azure AI Agent Service.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 5
+content: |
+  [!include[](includes/4-when-use-agent-service.md)]

learn-pr/wwl-data-ai/develop-ai-agent-azure/5-exercise.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.exercise
+title: Exercise - Build an AI agent
+metadata:
+  title: Exercise - Build an AI agent
+  description: Learn how to build an agent using Azure AI Agent Service. This unit provides a hands-on exercise to build an agent using Azure AI Agent Service.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 30
+content: |
+  [!include[](includes/5-exercise.md)]

learn-pr/wwl-data-ai/develop-ai-agent-azure/6-knowledge-check.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.knowledge-check
+title: Knowledge check
+metadata:
+  title: Knowledge check
+  description: This unit contains a knowledge check which can help learners reinforce their understanding of the material.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 3
+content: |
+quiz:
+  questions:
+  - content: "What is the first step in setting up Azure AI Agent Service?"  
+    choices:  
+      - content: "Deploy a compatible model"  
+        isCorrect: false  
+        explanation: "Incorrect. Deploying a compatible model is the second step. The first step is to create an Azure AI Foundry hub and project."  
+      - content: "Create an Azure AI Foundry hub and project"  
+        isCorrect: true  
+        explanation: "Correct. The first step in setting up Azure AI Agent Service is to create an Azure AI Foundry hub and project in your Azure subscription."  
+      - content: "Make API calls using SDKs"  
+        isCorrect: false  
+        explanation: "Incorrect. Making API calls using SDKs is the third step after deploying a compatible model."  
+  - content: "Which feature of Azure AI Agent Service ensures data privacy and compliance?"  
+    choices:  
+      - content: "Automatic tool calling"  
+        isCorrect: false  
+        explanation: "Incorrect. Automatic tool calling handles the tool-calling lifecycle but does not specifically ensure data privacy and compliance."  
+      - content: "Enterprise-grade security"  
+        isCorrect: true  
+        explanation: "Correct. Enterprise-grade security ensures data privacy and compliance with secure data handling and keyless authentication."  
+      - content: "Customizable storage solutions"  
+        isCorrect: false  
+        explanation: "Incorrect. Customizable storage solutions provide visibility and control over storage but do not specifically ensure data privacy and compliance."  
+  - content: "You are tasked with creating an AI agent that can interpret code and interact with data sources like Bing. Which feature of Azure AI Agent Service should you utilize?"  
+    choices:  
+      - content: "Out-of-the-box tools"  
+        isCorrect: true  
+        explanation: "Correct. Out-of-the-box tools include capabilities for code interpretation and interaction with data sources like Bing, making them ideal for this task."  
+      - content: "Securely managed data"  
+        isCorrect: false  
+        explanation: "Incorrect. Securely managed data handles conversation states but does not specifically provide tools for code interpretation or data source interaction."  
+      - content: "Enterprise-grade security"  
+        isCorrect: false  
+        explanation: "Incorrect. Enterprise-grade security ensures data privacy and compliance but does not specifically provide tools for code interpretation or data source interaction."  
+  

learn-pr/wwl-data-ai/develop-ai-agent-azure/7-summary.yml

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+### YamlMime:ModuleUnit
+uid: learn.wwl.develop-ai-agent-azure.summary
+title: Summary
+metadata:
+  title: Summary
+  description: This unit summarizes the key concepts covered in the module, including key features and how to use Azure AI Agent Service.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 02/24/2025
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/7-summary.md)]

learn-pr/wwl-data-ai/develop-ai-agent-azure/includes/1-introduction.md

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+Azure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality, extensible AI agents without needing to manage the underlying compute and storage resources.
+
+Imagine you're working in the healthcare industry, where there's a need to automate patient interactions and streamline administrative tasks. Your organization wants to develop an AI agent that can handle patient inquiries, schedule appointments, and provide medical information based on real-time data. However, managing the infrastructure and ensuring data security are significant challenges. Azure AI Agent Service offers a solution by allowing you to create AI agents tailored to your needs through custom instructions and advanced tools. This service simplifies the development process, reduces the amount of code required, and manages the underlying infrastructure, enabling you to focus on building high-quality AI solutions.
+
+In this module, you'll learn about the purpose, key features, setup process, and integration capabilities of Azure AI Agent Service.
+
+After completing this module, you’ll be able to:
+- Describe the purpose of AI agents
+- Explain the key features of Azure AI Agent Service
+- Build an agent using the Azure AI Agent Service
+- Integrate an agent in the Azure AI Agent Service into your own app

learn-pr/wwl-data-ai/develop-ai-agent-azure/includes/2-what-is-ai-agent.md

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+An AI agent is a software service that uses generative AI to understand and perform tasks on behalf of a user or another program. These agents leverage advanced AI models to understand context, make decisions, utilize grounding data, and take actions to achieve specific goals. Unlike traditional applications, AI agents can operate independently, executing complex workflows and automating processes without the need of constant human intervention. The evolution of generative AI enables agents to behave intelligently on our behalf, transforming how we can leverage and integrate these agents.
+
+Understanding what an AI agent is and how to utilize them is crucial for effectively using AI to automate tasks, make informed decisions, and enhance user experiences. This knowledge enables organizations to deploy AI agents strategically, maximizing their potential to drive innovation, improve efficiency, and achieve business objectives.
+
+## Why Are AI agents useful?
+
+AI agents are incredibly useful for several reasons:
+
+- **Automation of Routine Tasks**: AI agents can handle repetitive and mundane tasks, freeing up human workers to focus on more strategic and creative activities. This leads to increased productivity and efficiency.
+- **Enhanced Decision-Making**: By processing vast amounts of data and providing insights, AI agents support better decision-making. They can analyze trends, predict outcomes, and offer recommendations based on real-time data.
+- **Decision-Making Capabilities**: AI Agents can use advanced decision-making algorithms and machine learning models to analyze data and make informed decisions. This allows them to handle complex scenarios and provide actionable insights, whereas generative AI chat models primarily focus on generating text-based responses.
+- **Scalability**: AI agents can scale operations without the need for proportional increases in human resources. This is particularly beneficial for businesses looking to grow without significantly increasing operational costs.
+- **24/7 Availability**: Like all software, AI agents can operate continuously without breaks, ensuring that tasks are completed promptly and customer service is available around the clock.
+
+Agents are built to simulate human-like intelligence and can be applied in various domains such as customer service, data analysis, automation, and more.
+
+## Examples of AI agent use Casesc
+
+AI agents have a wide range of applications across various industries. Here are some notable examples:
+
+### Personal productivity agents
+
+Personal productivity agents assist individuals with daily tasks such as scheduling meetings, sending emails, and managing to-do lists. For instance, Microsoft 365 Copilot can help users draft documents, create presentations, and analyze data within the Microsoft Office suite.
+
+### Research agents
+
+Research agents continuously monitor market trends, gather data, and generate reports. These agents can be used in financial services to track stock performance, in healthcare to stay updated with the latest medical research, or in marketing to analyze consumer behavior.
+
+### Sales agents
+
+Sales agents automate lead generation and qualification processes. They can research potential leads, send personalized follow-up messages, and even schedule sales calls. This automation helps sales teams focus on closing deals rather than administrative tasks.
+
+### Customer service agents
+
+Customer service agents handle routine inquiries, provide information, and resolve common issues. They can be integrated into chatbots on websites or messaging platforms, offering instant support to customers. For example, Cineplex uses an AI agent to process refund requests, significantly reducing handling time and improving customer satisfaction.
+
+### Developer agents
+
+Developer agents help in software development tasks such as code review, bug fixing, and repository management. They can automatically update codebases, suggest improvements, and ensure that coding standards are maintained. GitHub Copilot is a great example of a developer agent.
+
+> [!TIP]
+> To learn more about GitHub Copilot, explore the [GitHub Copilot fundamentals](/training/paths/copilot/) learning path.
+
+## How to build an agent
+
+Agents can be built with various services and platforms, including Microsoft 365, Semantic Kernel, and Azure AI Agent Service. The rest of this module will focus on the AI Agent Service, however there are independent learning paths on Microsoft Learn for the other services.
+
+> [!TIP]
+> You can explore more about agents in general with the [Fundamentals of AI agents](/training/modules/ai-agent-fundamentals) module.