Merge pull request #49299 from ivorb/agent-service-modules

PR bugs

Author: v-dirichards

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

@@ -2,7 +2,7 @@ An AI agent is a software service that uses generative AI to understand and perf
 
 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?
+## Why Are AI agents useful?
 
 AI agents are incredibly useful for several reasons:
 
@@ -14,36 +14,36 @@ AI agents are incredibly useful for several reasons:
 
 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 Cases
+## 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
 
 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
 
 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
 
 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
 
 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
 
 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]
+> [!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]
+> [!TIP]
 > You can explore more about agents in general with the [Fundamentals of AI agents](/training/modules/ai-agent-fundamentals) module.

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

@@ -22,7 +22,7 @@ Previous solutions to achieve an agent-like experience took hundreds of lines of
 
 ## Agent building options
 
-Azure AI Agent Service offers several advantages to building agents, but isn't always the best solution for your use case. For example, if you're trying to build an integration with M365 you might choose the Copilot Studio agent builder and if you're trying to orchestrate multiple agents, you might choose the Semantic Kernel Agents Framework.
+Azure AI Agent Service offers several advantages to building agents, but isn't always the best solution for your use case. For example, if you're trying to build an integration with Microsoft 365 you might choose the Copilot Studio agent builder and if you're trying to orchestrate multiple agents, you might choose the Semantic Kernel Agents Framework.
 
 This [Fundamentals of AI Agents](/training/modules/ai-agent-fundamentals/3-agent-development) unit explores more of the options for building agents.