fixed blocking issues

Author: buzahid

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/6-knowledge-check.yml

@@ -39,9 +39,9 @@ quiz:
       - content: "Modify the AI model's architecture to integrate the plugin"  
         isCorrect: false  
         explanation: "Incorrect. Plugins work through function calling and don't require modifications to the AI model itself."  
-      - content: "Configure the plugin in Azure Portal before using it in Semantic Kernel"  
+      - content: "Configure the plugin in Azure portal before using it in Semantic Kernel"  
         isCorrect: false  
-        explanation: "Incorrect. Plugins are defined and added programmatically in the codebase, not configured in the Azure Portal."  
+        explanation: "Incorrect. Plugins are defined and added programmatically in the codebase, not configured in the Azure portal."  
       - content: "Define a class with methods annotated using the `kernel_function` decorator"  
         isCorrect: true  
         explanation: "Correct. The first step in using a plugin is defining a class and annotating its methods with the `kernel_function` decorator so the AI knows how to use them."  

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/1-introduction.md

@@ -12,6 +12,6 @@ In this module, you learn about the core features of the Semantic Kernel SDK and
 
 After completing this module, you're now able to:
 
-- Use Semantic Kernel to connect to an Azure AI Foundry project
-- Create Azure AI Agent Service agents using the Semantic Kernel SDK
-- Integrate plugin functions with your AI agent
+- Use Semantic Kernel to connect to an Azure AI Foundry project.
+- Create Azure AI Agent Service agents using the Semantic Kernel SDK.
+- Integrate plugin functions with your AI agent.

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/3-create-azure-ai-agent.md

@@ -5,12 +5,12 @@
 An AzureAIAgent object encapsulates all the core capabilities you typically use the Kernel for, like function execution, planning, and memory access. This object acts as a self-contained agent runtime.
 
 To use an AzureAIAgent:
-1. Create an Azure AI Foundry project
-1. Add the project connection string to your Semantic Kernel application code
-1. Create an AzureAIAgentSettings object
-1. Create an AzureAIAgent client
-1. Create an agent definition on the agent service provided by the client
-1. Create an agent based on the definition
+1. Create an Azure AI Foundry project.
+1. Add the project connection string to your Semantic Kernel application code.
+1. Create an AzureAIAgentSettings object.
+1. Create an AzureAIAgent client.
+1. Create an agent definition on the agent service provided by the client.
+1. Create an agent based on the definition.
 
 Here is the code that illustrates how to create an AzureAIAgent:
 

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/7-summary.md

@@ -2,6 +2,6 @@ In this module, you learned how the Semantic Kernel Agent Framework enables deve
 
 More reading:
 
-- Learn more about the [Semantic Kernel Agent Framework](https://learn.microsoft.com/semantic-kernel/frameworks/agent/?pivots=programming-language-python)
+- Learn more about the [Semantic Kernel Agent Framework](/semantic-kernel/frameworks/agent/?pivots=programming-language-python)
 - Learn more about [Azure AI Agent Service](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/introducing-azure-ai-agent-service/4298357)
-- Practice [developing generative AI apps with Semantic Kernel](https://learn.microsoft.com/training/paths/develop-ai-agents-azure-open-ai-semantic-kernel-sdk/)
+- Practice [developing generative AI apps with Semantic Kernel](/training/paths/develop-ai-agents-azure-open-ai-semantic-kernel-sdk/)