Merge branch 'main' into 2025-09-12-break-out-hub-project-docs

Author: jonburchel

.openpublishing.redirection.json

@@ -544,6 +544,36 @@
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       "redirect_url": "/azure/ai-foundry/foundry-models/concepts/endpoints",
       "redirect_document_id": true
+    },
+    {
+      "source_path": "articles/machine-learning/known-issues/application-sharing-policy-not-supported.md",
+      "redirect_url": "/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace?view=azureml-api-2",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/known-issues/compute-a10-sku-not-supported.md",
+      "redirect_url": "/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/known-issues/compute-idleshutdown-bicep.md",
+      "redirect_url": "/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/known-issues/compute-slowness-terminal-mounted-path.md",
+      "redirect_url": "/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/known-issues/jupyter-r-kernel-not-starting.md",
+      "redirect_url": "/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/known-issues/workspace-move-compute-instance-same-name.md",
+      "redirect_url": "/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/concepts/encryption-keys-portal.md

@@ -1,26 +1,28 @@
 ---
- title: Customer-Managed Keys for Azure AI Foundry
- titleSuffix: Azure AI Foundry
- description: Learn about using customer-managed keys for encryption to improve data security with Azure AI Foundry projects.
- ms.author: jburchel 
- author: jonburchel 
- ms.reviewer: deeikele
- ms.date: 09/12/2025
- ms.service: azure-ai-services
- ms.topic: concept-article
- ms.custom:
-   - ignite-2023
-   - build-aifnd
-   - build-2025
- ai-usage: ai-assisted
+title: Customer-Managed Keys for Azure AI Foundry
+titleSuffix: Azure AI Foundry
+description: Learn how to use customer-managed keys (CMK) for enhanced encryption and data security in Azure AI Foundry. Configure Azure Key Vault integration and meet compliance requirements.
+ms.author: jburchel 
+author: jonburchel 
+ms.reviewer: deeikele
+ms.date: 09/15/2025
+ms.service: azure-ai-services
+ms.topic: concept-article
+ms.custom:
+  - ignite-2023
+  - build-aifnd
+  - build-2025
+zone_pivot_groups: project-type
+ai-usage: ai-assisted
+# Customer intent: As an admin, I want to understand how I can use my own encryption keys with Azure AI Foundry.
 ---
 
 # Customer-managed keys for encryption with Azure AI Foundry (Foundry projects)
 
 > [!NOTE]
 > An alternate hub-focused CMK article is available: [Customer-managed keys for hub projects](hub-encryption-keys-portal.md).
 
-Customer-managed key (CMK) encryption in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) provides enhanced control over the encryption of your data. By using a CMK, you can manage your own encryption keys to add an extra layer of protection and meet compliance requirements more effectively.
+Customer-managed key (CMK) encryption in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) provides enhanced control over encryption of your data. Learn how to use customer-managed keys to add an extra layer of protection and meet compliance requirements more effectively with Azure Key Vault integration.
 
 ## About encryption in Azure AI Foundry
 

articles/ai-foundry/foundry-models/includes/models-azure-direct-others.md

@@ -27,7 +27,7 @@ The DeepSeek family of models includes DeepSeek-R1, which excels at reasoning ta
 
 | Model  | Type | Capabilities | Deployment type (region availability) | Project type |
 | ------ | ---- | ------------ | ------------------------------------- | ------------ |
-| [DeepSeek-V3.1](https://ai.azure.com/resource/models/DeepSeek-V3.1/version/1/registry/azureml-deepseek?cid=learnDocs) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON | - Global standard (all regions) <br> - Global provisioned (all regions) | Foundry, Hub-based |
+| [DeepSeek-V3.1](https://ai.azure.com/resource/models/DeepSeek-V3.1/version/1/registry/azureml-deepseek?cid=learnDocs) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON | - Global standard (all regions) | Foundry, Hub-based |
 | [DeepSeek-R1-0528](https://ai.azure.com/explore/models/deepseek-r1-0528/version/1/registry/azureml-deepseek?cid=learnDocs) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | - **Input:** text (163,840 tokens) <br /> - **Output:**  (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text. | - Global standard (all regions) <br> - Global provisioned (all regions)| Foundry, Hub-based |
 | [DeepSeek-V3-0324](https://ai.azure.com/explore/models/deepseek-v3-0324/version/1/registry/azureml-deepseek?cid=learnDocs) | chat-completion | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON | - Global standard (all regions) <br> - Global provisioned (all regions) | Foundry, Hub-based |
 | [DeepSeek-R1](https://ai.azure.com/explore/models/deepseek-r1/version/1/registry/azureml-deepseek?cid=learnDocs) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | - **Input:** text (163,840 tokens) <br /> - **Output:**  (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text. | - Global standard (all regions) <br> - Global provisioned (all regions) | Foundry, Hub-based |
@@ -95,4 +95,4 @@ See [the xAI model collection in Azure AI Foundry portal](https://ai.azure.com/e
 - [Deployment overview for Azure AI Foundry Models](../../concepts/deployments-overview.md)
 - [Add and configure models to Azure AI Foundry Models](../how-to/create-model-deployments.md)
 - [Deployment types in Azure AI Foundry Models](../concepts/deployment-types.md)
-- [Serverless API inference examples for Foundry Models](../../concepts/models-inference-examples.md)
+- [Serverless API inference examples for Foundry Models](../../concepts/models-inference-examples.md)

articles/ai-foundry/how-to/add-foundry-to-network-security-perimeter.md

@@ -1,5 +1,5 @@
 ---
-title: Add Azure AI Foundry to Network Security Perimeter
+title: Add Azure AI Foundry to Network Security Perimeter (preview)
 description: Discover how to secure your Azure AI Foundry resource by joining it to a network security perimeter, ensuring enhanced data protection and controlled access.
 author: jonburchel
 ms.author: jburchel
@@ -10,7 +10,7 @@ ms.service: azure-ai-foundry
 ai.usage: ai-assisted
 ---
 
-# Add Azure AI Foundry to a network security perimeter
+# Add Azure AI Foundry to a network security perimeter (preview)
 
 > [!NOTE]
 > Azure AI Foundry support for network security perimeter is in public preview under supplemental terms of use. It's available in regions providing the feature. This preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. Review the limitations and considerations section before you start.
@@ -45,6 +45,8 @@ You can add an Azure AI Foundry resource to a network security perimeter in the
 
 - The Foundry Agent Service supports [Network security perimeter](/azure/private-link/network-security-perimeter-concepts). However, in Secured Standard Agents with network isolation, NSP is neither required nor supported, as all resources connect securely via Private Link within the customer's virtual network, eliminating the need for public IP or FQDN definitions.
 
+- If you implemented Private Endpoints to another PaaS resource through Azure Private Link Service, the network traffic has a preference to check for Private Link and send traffic through that pathway first before NSP. Network traffic follows the order of Private Link before NSP. 
+
 ## Prerequisites
 
 > [!CAUTION]

articles/ai-foundry/how-to/develop/ai-template-get-started.md

@@ -6,7 +6,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 09/03/2025
+ms.date: 09/10/2025
 ms.reviewer: varundua
 ms.author: sgilley
 author: sdgilley
@@ -22,6 +22,12 @@ Available templates include:
 * [Get started with AI chat](https://github.com/Azure-Samples/get-started-with-ai-chat)
 * [Get started with AI agents](https://github.com/Azure-Samples/get-started-with-ai-agents)
 * [Unlock insights from conversational data](https://github.com/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator)
+* [Multi-agent workflow automation](https://github.com/microsoft/Multi-Agent-Custom-Automation-Engine-Solution-Accelerator)
+* [Multi-modal content processing](https://github.com/microsoft/content-processing-solution-accelerator)
+* [Generate documents from your data](https://github.com/microsoft/document-generation-solution-accelerator)
+* [Improve client meetings with agents](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator)
+* [Modernize your code with agents](https://github.com/microsoft/Modernize-your-code-solution-accelerator)
+* [Build your conversational agent](https://github.com/Azure-Samples/Azure-Language-OpenAI-Conversational-Agent-Accelerator)
 
 ## Prerequisites
 

articles/ai-foundry/how-to/develop/trace-agents-sdk.md

@@ -1,33 +1,33 @@
 ---
-title: How to trace your AI application
+title: View Trace Results for AI Agents in Azure AI Foundry
 titleSuffix: Azure AI Foundry
-description: This article provides instructions on how to trace your application with Azure AI Inference SDK.
+description: View trace results for AI agents using Azure AI Foundry SDK and OpenTelemetry. Learn to see execution traces, debug performance, and monitor AI agent behavior step-by-step.
 author: lgayhardt
 ms.author: lagayhar
 ms.reviewer: amibp
 ms.date: 08/21/2025
 ms.service: azure-ai-foundry
 ms.topic: how-to
-
+ai-usage: ai-assisted
 ---
 
-# Trace your AI agents using Azure AI Foundry portal and SDK (preview)
+# View trace results for AI agents in Azure AI Foundry (preview)
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-This article walks you through how to instrument tracing in agents using Azure AI Foundry SDK with OpenTelemetry and Azure Monitor for enhanced observability and debugging.
+Learn how to view trace results for AI agents in Azure AI Foundry. This article shows you how to see execution traces, analyze agent behavior, and debug performance issues using Azure AI Foundry SDK with OpenTelemetry and Azure Monitor for enhanced observability.
 
 Determining the reasoning behind your agent's executions is important for troubleshooting and debugging. However, it can be difficult for complex agents for a number of reasons:
 * There could be a high number of steps involved in generating a response, making it hard to keep track of all of them.
 * The sequence of steps might vary based on user input.
 * The inputs/outputs at each stage might be long and deserve more detailed inspection.
 * Each step of an agent's runtime might also involve nesting. For example, an agent might invoke a tool, which uses another process, which then invokes another tool. If you notice strange or incorrect output from a top-level agent run, it might be difficult to determine exactly where in the execution the issue was introduced.
 
-Tracing solves this by allowing you to clearly see the inputs and outputs of each primitive involved in a particular agent run, in the order in which they were invoked.
+Trace results solve this by allowing you to view the inputs and outputs of each primitive involved in a particular agent run, displayed in the order they were invoked, making it easy to understand and debug your AI agent's behavior.
 
-## Tracing in the Azure AI Foundry Agents playground
+## View trace results in the Azure AI Foundry Agents playground
 
-The Agents playground in the Azure AI Foundry portal lets you trace threads and runs that your agents produce. To open a trace, select **Thread logs** in an active thread. You can also optionally select **Metrics** to enable automatic evaluations of the model's performance across several dimensions of **AI quality** and **Risk and safety**.
+The Agents playground in the Azure AI Foundry portal lets you view trace results for threads and runs that your agents produce. To see trace results, select **Thread logs** in an active thread. You can also optionally select **Metrics** to enable automatic evaluations of the model's performance across several dimensions of **AI quality** and **Risk and safety**.
 
 > [!NOTE]
 > Evaluation results are available for 24 hours before expiring. To get evaluation results, select your desired metrics and chat with your agent.
@@ -39,14 +39,14 @@ The Agents playground in the Azure AI Foundry portal lets you trace threads and
 
 :::image type="content" source="../../media/trace/trace-agent-playground.png" alt-text="A screenshot of the agent playground in the Azure AI Foundry portal." lightbox="../../media/trace/trace-agent-playground.png":::
 
-After selecting **Thread logs**, the screen that appears will let you view the: thread, run, run steps and any tool calls that were made. You can view the inputs and outputs between the agent and user, as well the associated metadata and any evaluations you selected.
+After selecting **Thread logs**, you can view trace results including: thread details, run information, run steps and any tool calls that were made. The trace results show you the inputs and outputs between the agent and user, as well the associated metadata and any evaluations you selected.
 
 :::image type="content" source="../../agents/media/thread-trace.png" alt-text="A screenshot of a trace." lightbox="../../agents/media/thread-trace.png":::
 
 > [!TIP]
-> If you want to view the trace of a previous thread, select **My threads** in the **Agents** screen. Choose a thread, and then select **Try in playground**.
+> If you want to view trace results from a previous thread, select **My threads** in the **Agents** screen. Choose a thread, and then select **Try in playground**.
 > :::image type="content" source="../../agents/media/thread-highlight.png" alt-text="A screenshot of the threads screen." lightbox="../../agents/media/thread-highlight.png":::
-> You will be able to see the **Thread logs** button at the top of the screen to view the trace.
+> You will be able to see the **Thread logs** button at the top of the screen to view the trace results.
 
 
 > [!NOTE]

articles/ai-foundry/how-to/develop/trace-application.md

@@ -1,20 +1,21 @@
 ---
-title: How to trace AI applications using OpenAI SDK
+title: View Trace Results for AI Applications using OpenAI SDK
 titleSuffix: Azure AI Foundry
-description: Learn how to trace applications that use OpenAI SDK in Azure AI Foundry
+description: View trace results for AI applications using OpenAI SDK with OpenTelemetry in Azure AI Foundry. See execution traces, diagnose issues, and monitor application performance.
 author: lgayhardt
 ms.author: lagayhar
 ms.reviewer: ychen
 ms.date: 08/29/2025
 ms.service: azure-ai-foundry
 ms.topic: how-to
+ai-usage: ai-assisted
 ---
 
-# Trace AI applications using OpenAI SDK
+# View trace results for AI applications using OpenAI SDK
 
-Tracing provides deep visibility into execution of your application by capturing detailed telemetry at each execution step. This helps diagnose issues and enhance performance by identifying problems such as inaccurate tool calls, misleading prompts, high latency, low-quality evaluation scores, and more.  
+Learn how to view trace results that provide deep visibility into AI application execution. See detailed telemetry captured at each step to diagnose issues and enhance performance by identifying problems such as inaccurate tool calls, misleading prompts, high latency, and low-quality evaluation scores.  
 
-This article explains how to implement tracing for AI applications using **OpenAI SDK** with OpenTelemetry in Azure AI Foundry.
+This article explains how to view trace results for AI applications using **OpenAI SDK** with OpenTelemetry in Azure AI Foundry.
 
 ## Prerequisites
 
@@ -56,13 +57,38 @@ The following steps show how to configure your resource:
 
     1. Once the connection is configured, you're ready to use tracing in any project within the resource.
 
+    > [!TIP]
+    > Make sure you have the [Log Analytics Reader role](/azure/azure-monitor/logs/manage-access?tabs=portal#log-analytics-reader) assigned in your Application Insights resource. To learn more on how to assign roles, see [Assign Azure roles using the Azure portal](/azure/role-based-access-control/role-assignments-portal).
+
 1. Go to the landing page of your project and copy the project's endpoint URI. You need it later.
 
     :::image type="content" source="../../media/how-to/projects/fdp-project-overview.png" alt-text="A screenshot showing how to copy the project endpoint URI." lightbox="../../media/how-to/projects/fdp-project-overview.png":::
 
     > [!IMPORTANT]
     > Using a project's endpoint requires configuring Microsoft Entra ID in your application. If you don't have Entra ID configured, use the Azure Application Insights connection string as indicated in step 3 of the tutorial.
 
+## View trace results in Azure AI Foundry portal
+
+Once you have tracing configured and your application is instrumented, you can view trace results in the Azure AI Foundry portal:
+
+1. Go to [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) and navigate to your project.
+
+1. On the side navigation bar, select **Tracing**.
+
+1. You'll see a list of trace results from your instrumented applications. Each trace shows:
+   - **Trace ID**: Unique identifier for the trace
+   - **Start time**: When the trace began
+   - **Duration**: How long the operation took
+   - **Status**: Success or failure status
+   - **Operations**: Number of spans in the trace
+
+1. Select any trace to view detailed trace results including:
+   - Complete execution timeline
+   - Input and output data for each operation
+   - Performance metrics and timing
+   - Error details if any occurred
+   - Custom attributes and metadata
+
 ## Instrument the OpenAI SDK
 
 When developing with the OpenAI SDK, you can instrument your code so traces are sent to Azure AI Foundry. Follow these steps to instrument your code:
@@ -123,7 +149,7 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
     )
     ```
 
-1. If you go back to Azure AI Foundry portal, you should see the trace displayed:
+1. If you go back to Azure AI Foundry portal, you can view the trace results:
 
     :::image type="content" source="../../media/how-to/develop/trace-application/tracing-display-simple.png" alt-text="A screenshot showing how a simple chat completion request is displayed in the trace." lightbox="../../media/how-to/develop/trace-application/tracing-display-simple.png":::
 
@@ -164,7 +190,7 @@ When developing with the OpenAI SDK, you can instrument your code so traces are
         return responses
     ```
 
-1. Traces look as follows:
+1. Trace results look as follows:
 
     :::image type="content" source="../../media/how-to/develop/trace-application/tracing-display-decorator.png" alt-text="A screenshot showing how a method using a decorator is displayed in the trace." lightbox="../../media/how-to/develop/trace-application/tracing-display-decorator.png":::