Merge branch 'main' of github.com:MicrosoftDocs/azure-ai-docs-pr into evalprotal0825

Author: lgayhardt

.gitignore

@@ -20,3 +20,5 @@ _repo.*/
 .github/prompts/*.md
 .github/prompts/*.zip
 .github/patterns/*.md
+
+articles/ai-foundry/includes/get-started-fdp.md

.openpublishing.redirection.json

@@ -7,7 +7,7 @@
     },
     {
       "source_path": "articles/ai-foundry/how-to/develop/connections-add-sdk.md",
-      "redirect_url": "/azure/ai-foundry/how-to/connections-add?pivots=hub-project",
+      "redirect_url": "/azure/ai-foundry/how-to/connections-add",
       "redirect_document_id": false
     },
     {
@@ -300,6 +300,16 @@
       "redirect_url": "/azure/ai-foundry",
       "redirect_document_id": false
     },
+    {
+      "source_path": "articles/ai-foundry/openai/concepts/models.md",
+      "redirect_url": "/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?pivots=azure-openai",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/ai-foundry/foundry-models/concepts/models.md",
+      "redirect_url": "/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?pivots=azure-direct-others",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/speech-service/text-to-speech-avatar/custom-avatar-endpoint.md",
       "redirect_url": "/azure/ai-services/speech-service/custom-avatar-create",
@@ -534,6 +544,36 @@
       "source_path": "articles/ai-foundry/foundry-models/how-to/inference.md",
       "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/agents/how-to/tools/bing-code-samples.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-agent-service
 ms.topic: how-to
-ms.date: 07/11/2025
+ms.date: 09/09/2025
 author: aahill
 ms.author: aahi
 zone_pivot_groups: selection-bing-grounding-code
@@ -173,8 +173,8 @@ using Azure.AI.Agents.Persistent;
 using Azure.Identity;
 
 var projectEndpoint = System.Environment.GetEnvironmentVariable("ProjectEndpoint");
-var projectEndpoint = System.Environment.GetEnvironmentVariable("ModelDeploymentName");
-var projectEndpoint = System.Environment.GetEnvironmentVariable("BingConnectionId");
+var modelDeploymentName = System.Environment.GetEnvironmentVariable("ModelDeploymentName");
+var bingConnectionId = System.Environment.GetEnvironmentVariable("BingConnectionId");
 
 // Create the Agent Client
 PersistentAgentsClient agentClient = new(projectEndpoint, new DefaultAzureCredential());

articles/ai-foundry/agents/how-to/tools/bing-custom-search-samples.md

@@ -55,9 +55,9 @@ Use this article to find step-by-step instructions and code samples for using th
 
 * The name of your Grounding with Bing Custom Search configuration, which contains the URLs you want to allow or disallow. You can find it by navigating to the overview page for your resource in the [Azure portal](https://portal.azure.com/). Select **Configurations**, then select your configuration. 
 
-    :::image type="content" source="../../media/tools/bing/custom-connection-name.png" alt-text="A screenshot showing the Grounding with Bing Custom Search connection name. " lightbox="../../media/tools/bing/custom-connection-name.png":::
+    :::image type="content" source="../../media/tools/bing/custom-connection-name.png" alt-text="A screenshot showing the Grounding with Bing Custom Search configuration name. " lightbox="../../media/tools/bing/custom-connection-name.png":::
 
-    Save this connection name to an environment variable named `BING_CUSTOM_CONNECTION_NAME`. 
+    Save this configuration name to an environment variable named `BING_CUSTOM_INSTANCE_NAME`. 
 
 * The names of your model's deployment name. You can find it in **Models + Endpoints** in the left navigation menu. 
 

articles/ai-foundry/agents/how-to/tools/browser-automation.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-agent-service
 ms.topic: how-to
-ms.date: 08/12/2025
+ms.date: 09/15/2025
 author: aahill
 ms.author: aahi
 ms.custom: azure-ai-agents
@@ -65,7 +65,7 @@ An example flow would be:
 
         * **Key**: [Get the Playwright access token](https://aka.ms/pww/docs/generate-access-token)
 
-    For more information on creating a connection, see [Create a connection](../../../how-to/connections-add.md?pivots=fdp-project).
+    For more information on creating a connection, see [Create a connection](../../../how-to/connections-add.md).
 
 1. Configure your client by adding a Browser Automation tool using the Azure Playwright connection ID.
 

articles/ai-foundry/agents/how-to/tools/computer-use-samples.md

@@ -0,0 +1,222 @@
+---
+title: 'How to use the Computer Use Tool'
+titleSuffix: Azure AI Foundry
+description: Find code samples and instructions for using the Computer Use model in the Azure AI Foundry Agent Service.
+services: cognitive-services
+manager: nitinme
+ms.service: azure-ai-agent-service
+ms.topic: how-to
+ms.date: 08/22/2025
+author: aahill
+ms.author: aahi
+---
+
+# How to use the Computer Use Tool
+
+Use this article to learn how to use the Computer Use tool with the Azure AI Projects SDK.
+
+## Prerequisites
+
+* The requirements in the [Computer Use Tool overview](./deep-research.md).
+* Your Azure AI Foundry Project endpoint.
+
+    
+    [!INCLUDE [endpoint-string-portal](../../includes/endpoint-string-portal.md)]
+
+    Save this endpoint to an environment variable named `PROJECT_ENDPOINT`.
+
+* The deployment name of your Computer Use model. You can find it in **Models + Endpoints** in the left navigation menu.
+
+   :::image type="content" source="../../media/tools/computer-use-model-deployment.png" alt-text="A screenshot showing the model deployment screen the AI Foundry portal." lightbox="../../media/tools/computer-use-model-deployment.png":::
+    
+    Save the name of your model's deployment name as an environment variable named `COMPUTER_USE_MODEL_DEPLOYMENT_NAME`.
+
+* Before using the tool, you need to set up an environment that can capture screenshots and execute the recommended actions by the agent. We recommend using a sandboxed environment, such as Playwright for safety reasons.
+
+The Computer Use tool requires the latest prerelease versions of the `azure-ai-projects` library. First we recommend creating a [virtual environment](https://docs.python.org/3/library/venv.html) to work in:
+
+```console
+python -m venv env
+# after creating the virtual environment, activate it with:
+.\env\Scripts\activate
+```
+
+You can install the package with the following command:
+
+```console
+pip install --pre azure-ai-projects, azure-identity, azure-ai-agents 
+```
+
+## Code example
+
+The following code sample shows a basic API request. Once the initial API request is sent, you would perform a loop where the specified action is performed in your application code, sending a screenshot with each turn so the model can evaluate the updated state of the environment. You can see an example integration for a similar API in the [Azure OpenAI documentation](../../../openai/how-to/computer-use.md#playwright-integration). 
+
+```python
+import os, time, base64
+from typing import List
+from azure.ai.agents.models._models import ComputerScreenshot, TypeAction
+from azure.ai.projects import AIProjectClient
+from azure.ai.agents.models import (
+    MessageRole,
+    RunStepToolCallDetails,
+    RunStepComputerUseToolCall,
+    ComputerUseTool,
+    ComputerToolOutput,
+    MessageInputContentBlock,
+    MessageImageUrlParam,
+    MessageInputTextBlock,
+    MessageInputImageUrlBlock,
+    RequiredComputerUseToolCall,
+    SubmitToolOutputsAction,
+)
+from azure.identity import DefaultAzureCredential
+
+def image_to_base64(image_path: str) -> str:
+    """
+    Convert an image file to a Base64-encoded string.
+
+    :param image_path: The path to the image file (e.g. 'image_file.png')
+    :return: A Base64-encoded string representing the image.
+    :raises FileNotFoundError: If the provided file path does not exist.
+    :raises OSError: If there's an error reading the file.
+    """
+    if not os.path.isfile(image_path):
+        raise FileNotFoundError(f"File not found at: {image_path}")
+
+    try:
+        with open(image_path, "rb") as image_file:
+            file_data = image_file.read()
+        return base64.b64encode(file_data).decode("utf-8")
+    except Exception as exc:
+        raise OSError(f"Error reading file '{image_path}'") from exc
+
+asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/cua_screenshot.jpg"))
+action_result_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/cua_screenshot_next.jpg"))
+project_client = AIProjectClient(endpoint=os.environ["PROJECT_ENDPOINT"], credential=DefaultAzureCredential())
+
+# Initialize Computer Use tool with a browser-sized viewport
+environment = os.environ.get("COMPUTER_USE_ENVIRONMENT", "windows")
+computer_use = ComputerUseTool(display_width=1026, display_height=769, environment=environment)
+
+with project_client:
+
+    agents_client = project_client.agents
+
+    # Create a new Agent that has the Computer Use tool attached.
+    agent = agents_client.create_agent(
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        name="my-agent-computer-use",
+        instructions="""
+            You are an computer automation assistant. 
+            Use the computer_use_preview tool to interact with the screen when needed.
+            """,
+        tools=computer_use.definitions,
+    )
+
+    print(f"Created agent, ID: {agent.id}")
+
+    # Create thread for communication
+    thread = agents_client.threads.create()
+    print(f"Created thread, ID: {thread.id}")
+
+    input_message = (
+        "I can see a web browser with bing.com open and the cursor in the search box."
+        "Type 'movies near me' without pressing Enter or any other key. Only type 'movies near me'."
+    )
+    image_base64 = image_to_base64(asset_file_path)
+    img_url = f"data:image/jpeg;base64,{image_base64}"
+    url_param = MessageImageUrlParam(url=img_url, detail="high")
+    content_blocks: List[MessageInputContentBlock] = [
+        MessageInputTextBlock(text=input_message),
+        MessageInputImageUrlBlock(image_url=url_param),
+    ]
+    # Create message to thread
+    message = agents_client.messages.create(thread_id=thread.id, role=MessageRole.USER, content=content_blocks)
+    print(f"Created message, ID: {message.id}")
+
+    run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id)
+    print(f"Created run, ID: {run.id}")
+
+    # create a fake screenshot showing the text typed in
+    result_image_base64 = image_to_base64(action_result_file_path)
+    result_img_url = f"data:image/jpeg;base64,{result_image_base64}"
+    computer_screenshot = ComputerScreenshot(image_url=result_img_url)
+
+    while run.status in ["queued", "in_progress", "requires_action"]:
+        time.sleep(1)
+        run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)
+
+        if run.status == "requires_action" and isinstance(run.required_action, SubmitToolOutputsAction):
+            print("Run requires action:")
+            tool_calls = run.required_action.submit_tool_outputs.tool_calls
+            if not tool_calls:
+                print("No tool calls provided - cancelling run")
+                agents_client.runs.cancel(thread_id=thread.id, run_id=run.id)
+                break
+
+            tool_outputs = []
+            for tool_call in tool_calls:
+                if isinstance(tool_call, RequiredComputerUseToolCall):
+                    print(tool_call)
+                    try:
+                        action = tool_call.computer_use_preview.action
+                        print(f"Executing computer use action: {action.type}")
+                        if isinstance(action, TypeAction):
+                            print(f"  Text to type: {action.text}")
+                            #(add hook to input text in managed environment API here)
+
+                            tool_outputs.append(
+                                ComputerToolOutput(tool_call_id=tool_call.id, output=computer_screenshot)
+                            )
+                        if isinstance(action, ComputerScreenshot):
+                            print(f"  Screenshot requested")
+                            # (add hook to take screenshot in managed environment API here)
+
+                            tool_outputs.append(
+                                ComputerToolOutput(tool_call_id=tool_call.id, output=computer_screenshot)
+                            )
+                    except Exception as e:
+                        print(f"Error executing tool_call {tool_call.id}: {e}")
+
+            print(f"Tool outputs: {tool_outputs}")
+            if tool_outputs:
+                agents_client.runs.submit_tool_outputs(thread_id=thread.id, run_id=run.id, tool_outputs=tool_outputs)
+
+        print(f"Current run status: {run.status}")
+
+    print(f"Run completed with status: {run.status}")
+    if run.status == "failed":
+        print(f"Run failed: {run.last_error}")
+
+    # Fetch run steps to get the details of the agent run
+    run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id)
+    for step in run_steps:
+        print(f"Step {step.id} status: {step.status}")
+        print(step)
+
+        if isinstance(step.step_details, RunStepToolCallDetails):
+            print("  Tool calls:")
+            run_step_tool_calls = step.step_details.tool_calls
+
+            for call in run_step_tool_calls:
+                print(f"    Tool call ID: {call.id}")
+                print(f"    Tool call type: {call.type}")
+
+                if isinstance(call, RunStepComputerUseToolCall):
+                    details = call.computer_use_preview
+                    print(f"    Computer use action type: {details.action.type}")
+
+                print()  # extra newline between tool calls
+
+        print()  # extra newline between run steps
+
+    # Optional: Delete the agent once the run is finished.
+    agents_client.delete_agent(agent.id)
+    print("Deleted agent")
+```
+
+## Next steps
+
+* [Python agent samples](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/microsoft/python/getting-started-agents)
+* [Azure OpenAI Computer Use example Playwright integration](../../../openai/how-to/computer-use.md#playwright-integration)
+    * The Azure OpenAI API has implementation differences compared to the Agent Service, and these examples may need to be adapted to work with agents.