extension updates to unit

Author: ivorb

learn-pr/wwl-data-ai/prepare-azure-ai-development/includes/4-azure-ai-foundry.md

@@ -14,11 +14,11 @@ In Microsoft Foundry, you manage the resource connections, data, code, and other
 
 A Microsoft Foundry resource supports the most common AI development tasks to develop generative AI chat apps and agents. In most cases, using a Foundry project provides the right level of resource centralization and capabilities with a minimal amount of administrative resource management. You can use Microsoft Foundry portal to work in projects that are based in Microsoft Foundry resources, making it easy to add connected resources and manage model and agent deployments.
 
-### Hub-based projects
+### Hub-based projects (deprecated)
 
 ![Diagram of a hub-based project.](../media/hub-project.png)
 
-*Hub-based projects* are associated with an **Azure AI hub** resource in an Azure subscription. Hub-based projects include a Microsoft Foundry resource, as well as managed compute, support for Prompt Flow development, and connected **Azure storage** and **Azure key vault** resources for secure data storage.
+*Hub-based projects* are associated with an **Azure AI hub** resource in an Azure subscription. Hub-based projects include a Microsoft Foundry resource, as well as managed compute, support for Prompt Flow development, and connected **Azure storage** and **Azure key vault** resources for secure data storage. These are now deprecated, but still in use in several projects today.
 
 Azure AI hub resources support advanced AI development scenarios, like developing Prompt Flow based applications or fine-tuning models. You can also use Azure AI hub resources in both Microsoft Foundry portal and Azure Machine learning portal, making it easier to work on collaborative projects that involve data scientists and machine learning specialists as well as developers and AI software engineers
 

learn-pr/wwl-data-ai/prepare-azure-ai-development/includes/5-tools-and-sdks.md

@@ -8,15 +8,16 @@ There are many development tools and environments available, and developers shou
 
 When developing Microsoft Foundry based generative AI applications in Visual Studio Code, you can use the Microsoft Foundry for Visual Studio Code extension to simplify key tasks in the workflow, including:
 
-- Creating a project.
-- Selecting and deploying a model.
-- Testing a model in the playground.
-- Creating an agent.
+- Browsing and managing project resources, including deployed models, agents, connections, and vector stores.
+- Deploying models from the model catalog.
+- Testing models and agents in integrated playgrounds.
+- Configuring declarative and hosted agents using a visual designer and YAML files.
+- Generating integration code to connect agents with your applications.
 
-![Screenshot of the Microsoft Foundry Visual Studio Code extension.](../media/vs-code.png)
+![Screenshot of the Microsoft Foundry Visual Studio Code extension showing the Resources, Tools, and Help and Feedback sections.](../media/vs-code.png)
 
 > [!TIP]
-> For more information about using the Microsoft Foundry for Visual Studio Code extension, see **[Work with the Microsoft Foundry for Visual Studio Code extension](/azure/ai-foundry/how-to/develop/get-started-projects-vs-code?azure-portal=true)**.
+> For more information about using the Microsoft Foundry for Visual Studio Code extension, see **[Work with the Microsoft Foundry for Visual Studio Code extension](/azure/ai-foundry/how-to/develop/get-started-projects-vs-code?azure-portal=true)**. To learn how to develop AI agents and use the extension, see the [Develop AI agents with Azure and Visual Studio Code](/training/modules/develop-ai-agents-azure-vs-code) module.
 
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