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

Author: sdgilley

.openpublishing.redirection.json

@@ -489,6 +489,26 @@
       "source_path": "articles/ai-foundry/model-inference/index.yml",
       "redirect_url": "../foundry-models/index",
       "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/concept-package-models.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/how-to-package-models-moe.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/how-to-package-models-app-service.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/machine-learning/how-to-package-models.md",
+      "redirect_url": "/azure/machine-learning/concept-endpoints",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/agents/concepts/threads-runs-messages.md

@@ -34,11 +34,11 @@ A custom AI that uses AI models in conjunction with tools.
 
 ## Threads
 
-Threads are conversation sessions between an agent and a user. They store messages and automatically handle truncation to fit content into a model’s context. When you create a thread, you can append new messages to it as users respond.
+Threads are conversation sessions between an agent and a user. They store messages and automatically handle truncation to fit content into a model’s context. When you create a thread, you can append new messages (maximum of 100,000 per thread) to it as users respond.
 
 ## Messages
 
-Messages are the individual pieces of communication within a thread. They can be created by either the agent or the user and can include text, or other files. Messages are stored as a list within the thread, allowing for a structured and organized conversation flow.
+Messages are the individual pieces of communication within a thread. They can be created by either the agent or the user and can include text, or other files. Messages are stored as a list within the thread, allowing for a structured and organized conversation flow. You can attach up to 100,000 messages to a single thread.
 
 ## Runs
 

articles/ai-foundry/agents/how-to/connected-agents.md

@@ -230,7 +230,7 @@ To create a multi-agent setup, follow these steps:
     ```python
     import os
     from azure.ai.projects import AIProjectClient
-    from azure.ai.projects.models import ConnectedAgentTool, MessageRole
+    from azure.ai.agents.models import ConnectedAgentTool, MessageRole
     from azure.identity import DefaultAzureCredential
 
 

articles/ai-foundry/agents/how-to/metrics.md

@@ -15,6 +15,8 @@ ms.service: azure-ai-agent-service
 
 Monitoring is available for agents in a [standard agent setup](../concepts/standard-agent-setup.md).
 
+[!INCLUDE [Feature preview](../../openai/includes/preview-feature.md)]
+
 > [!IMPORTANT]
 > Monitoring support is currently limited to Azure AI Foundry hubs. Azure AI Foundry projects are not supported.
 
@@ -94,4 +96,4 @@ You can set alerts for any metric listed in the [monitoring data reference](../r
 ## Related content
 
 - See [Monitoring data reference](../reference/monitor-service.md) for a reference of the metrics and other important values created for Azure AI Foundry Agent Service.
-- See [Monitoring Azure resources with Azure Monitor](/azure/azure-monitor/essentials/monitor-azure-resource) for general details on monitoring Azure resources.
+- See [Monitoring Azure resources with Azure Monitor](/azure/azure-monitor/essentials/monitor-azure-resource) for general details on monitoring Azure resources.

articles/ai-foundry/agents/how-to/tools/azure-functions.md

@@ -14,10 +14,14 @@ ms.custom: azure-ai-agents
 
 # Use Azure Functions with Azure AI Foundry Agent Service
 
-The Azure AI Foundry Agent Service integrates with Azure Functions, enabling you to create intelligent, event-driven applications with minimal overhead. This combination allows AI-driven workflows to leverage the scalability and flexibility of serverless computing, making it easier to build and deploy solutions that respond to real-time events or complex workflows. 
- 
-Azure Functions provide support for triggers and bindings, which simplify how your AI Agents interact with external systems and services. Triggers determine when a function executes—such as an HTTP request, message from a queue, or a file upload to Azure Blob Storage and allows agents to act dynamically based on incoming events. 
- 
+The Azure AI Foundry Agent Service integrates with Azure Functions, enabling you to create intelligent, event-driven applications with minimal overhead. This combination allows AI-driven workflows to leverage the scalability and flexibility of serverless computing, making it easier to build and deploy solutions that respond to real-time events or complex workflows.
+
+Currently, direct integration with Azure Functions is only supported for functions triggered by Azure Storage Queues. Other trigger types, such as HTTP or Blob Storage, are not natively supported at this time.
+
+Azure Functions provide support for triggers and bindings, which simplify how your AI Agents interact with external systems and services. Triggers determine when a function executes—such as an HTTP request, message from a queue, or a file upload to Azure Blob Storage—and allow agents to act dynamically based on incoming events.
+
+For HTTP-triggered Azure Functions, integration is possible by describing the function through an OpenAPI specification and registering it as a callable tool in the agent configuration. Alternatively, you can implement a queue-based wrapper function that receives messages from the agent and internally invokes the HTTP logic, enabling the use of the existing queue-based integration.
+
 Meanwhile, bindings facilitate streamlined connections to input or output data sources, such as databases or APIs, without requiring extensive boilerplate code. For instance, you can configure a trigger to execute an Azure Function whenever a customer message is received in a chatbot and use output bindings to send a response via the Azure AI Agent.
 
 ### Supported models