Merge with main

dotnet/samples/01-get-started/01_hello_agent/Program.cs

@@ -8,7 +8,7 @@ using Microsoft.Agents.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

dotnet/samples/01-get-started/02_add_tools/Program.cs

@@ -11,7 +11,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)

dotnet/samples/01-get-started/03_multi_turn/Program.cs

@@ -8,7 +8,7 @@ using Microsoft.Agents.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

dotnet/samples/01-get-started/04_memory/Program.cs

@@ -16,7 +16,7 @@ using OpenAI.Chat;
 using SampleApp;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -50,12 +50,12 @@ Console.WriteLine(await agent.RunAsync("My name is Ruaidhrí", session));
 Console.WriteLine(await agent.RunAsync("I am 20 years old", session));
 
 // We can serialize the session. The serialized state will include the state of the memory component.
-JsonElement sesionElement = await agent.SerializeSessionAsync(session);
+JsonElement sessionElement = await agent.SerializeSessionAsync(session);
 
 Console.WriteLine("\n>> Use deserialized session with previously created memories\n");
 
 // Later we can deserialize the session and continue the conversation with the previous memory component state.
-var deserializedSession = await agent.DeserializeSessionAsync(sesionElement);
+var deserializedSession = await agent.DeserializeSessionAsync(sessionElement);
 Console.WriteLine(await agent.RunAsync("What is my name and age?", deserializedSession));
 
 Console.WriteLine("\n>> Read memories using memory component\n");

dotnet/samples/01-get-started/06_host_your_agent/Program.cs

@@ -8,7 +8,7 @@
 //
 // Environment variables:
 //   AZURE_OPENAI_ENDPOINT
-//   AZURE_OPENAI_DEPLOYMENT_NAME (defaults to "gpt-4o-mini")
+//   AZURE_OPENAI_DEPLOYMENT_NAME (defaults to "gpt-5.4-mini")
 //
 // Run with: func start
 // Then call: POST http://localhost:7071/api/agents/HostedAgent/run
@@ -23,7 +23,7 @@ using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
     ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Set up an AI agent following the standard Microsoft Agent Framework pattern.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/A2A/A2AAgent_AsFunctionTools/Program.cs

@@ -13,7 +13,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 var a2aAgentHost = Environment.GetEnvironmentVariable("A2A_AGENT_HOST") ?? throw new InvalidOperationException("A2A_AGENT_HOST is not set.");
 
 // Initialize an A2ACardResolver to get an A2A agent card.

dotnet/samples/02-agents/A2A/A2AAgent_AsFunctionTools/README.md

@@ -18,5 +18,5 @@ Set the following environment variables:
 ```powershell
 $env:A2A_AGENT_HOST="https://your-a2a-agent-host" # Replace with your A2A agent host endpoint
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/A2A/A2AAgent_PollingForTaskCompletion/A2AAgent_PollingForTaskCompletion.csproj

@@ -2,7 +2,7 @@
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
-    <TargetFramework>net10.0</TargetFramework>
+    <TargetFrameworks>net10.0</TargetFrameworks>
 
     <Nullable>enable</Nullable>
     <ImplicitUsings>enable</ImplicitUsings>
@@ -13,7 +13,6 @@
     <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Microsoft.Extensions.Hosting" />
-    <PackageReference Include="System.Net.ServerSentEvents" />
   </ItemGroup>
 
   <ItemGroup>

dotnet/samples/02-agents/A2A/A2AAgent_PollingForTaskCompletion/Program.cs

@@ -18,8 +18,12 @@ AIAgent agent = agentCard.AsAIAgent();
 
 AgentSession session = await agent.CreateSessionAsync();
 
+// AllowBackgroundResponses must be true so the server returns immediately with a continuation token
+// instead of blocking until the task is complete.
+AgentRunOptions options = new() { AllowBackgroundResponses = true };
+
 // Start the initial run with a long-running task.
-AgentResponse response = await agent.RunAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", session);
+AgentResponse response = await agent.RunAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", session, options: options);
 
 // Poll until the response is complete.
 while (response.ContinuationToken is { } token)

dotnet/samples/02-agents/A2A/A2AAgent_ProtocolSelection/A2AAgent_ProtocolSelection.csproj

@@ -0,0 +1,19 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="A2A" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.A2A\Microsoft.Agents.AI.A2A.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/A2A/A2AAgent_ProtocolSelection/Program.cs

@@ -0,0 +1,36 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to select the A2A protocol binding (HTTP+JSON vs JSON-RPC) when
+// creating an AIAgent from an A2A agent card using A2AClientOptions.PreferredBindings.
+
+using A2A;
+using Microsoft.Agents.AI;
+
+var a2aAgentHost = Environment.GetEnvironmentVariable("A2A_AGENT_HOST") ?? throw new InvalidOperationException("A2A_AGENT_HOST is not set.");
+
+// Initialize an A2ACardResolver to get an A2A agent card.
+A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
+
+// Get the agent card
+AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();
+
+// Use A2AClientOptions to explicitly select the HTTP+JSON protocol binding.
+// This tells the A2A client factory to prefer the HTTP+JSON interface when the agent card
+// advertises multiple supported interfaces.
+A2AClientOptions options = new()
+{
+    PreferredBindings = [ProtocolBindingNames.HttpJson]
+};
+
+// To prefer JSON-RPC instead, use:
+// A2AClientOptions options = new()
+// {
+//     PreferredBindings = [ProtocolBindingNames.JsonRpc]
+// };
+
+// Create an instance of the AIAgent for an existing A2A agent, using the specified protocol binding.
+AIAgent agent = agentCard.AsAIAgent(options: options);
+
+// Invoke the agent and output the text result.
+AgentResponse response = await agent.RunAsync("Tell me a joke about a pirate.");
+Console.WriteLine(response);

dotnet/samples/02-agents/A2A/A2AAgent_ProtocolSelection/README.md

@@ -0,0 +1,27 @@
+# A2A Agent Protocol Selection
+
+This sample demonstrates how to select the A2A protocol binding when creating an `AIAgent` from an A2A agent card.
+
+A2A agents can expose multiple interfaces with different protocol bindings (e.g., HTTP+JSON, JSON-RPC). By default, `AsAIAgent()` prefers HTTP+JSON with JSON-RPC as a fallback. This sample shows how to use `A2AClientOptions.PreferredBindings` to explicitly control which protocol binding is used.
+
+The sample:
+
+- Connects to an A2A agent server specified in the `A2A_AGENT_HOST` environment variable
+- Configures `A2AClientOptions` to prefer the HTTP+JSON protocol binding
+- Creates an `AIAgent` from the resolved agent card using the specified binding
+- Sends a message to the agent and displays the response
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10.0 SDK or later
+- An A2A agent server running and accessible via HTTP
+
+**Note**: These samples need to be run against a valid A2A server. If no A2A server is available, they can be run against the echo-agent that can be spun up locally by following the guidelines at: https://github.com/a2aproject/a2a-dotnet/blob/main/samples/AgentServer/README.md
+
+Set the following environment variable:
+
+```powershell
+$env:A2A_AGENT_HOST="http://localhost:5000"  # Replace with your A2A agent server host
+```

dotnet/samples/02-agents/A2A/A2AAgent_StreamReconnection/A2AAgent_StreamReconnection.csproj

@@ -6,18 +6,18 @@
 
     <Nullable>enable</Nullable>
     <ImplicitUsings>enable</ImplicitUsings>
-    <UserSecretsId>3afc9b74-af74-4d8e-ae96-fa1c511d11ac</UserSecretsId>
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.Agents.Persistent" />
+    <PackageReference Include="A2A" />
+    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
     <PackageReference Include="Microsoft.Extensions.Hosting" />
-    <PackageReference Include="ModelContextProtocol" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.A2A\Microsoft.Agents.AI.A2A.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
   </ItemGroup>
-  
+
 </Project>

dotnet/samples/02-agents/A2A/A2AAgent_StreamReconnection/Program.cs

@@ -0,0 +1,55 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to reconnect to an A2A agent's streaming response using continuation tokens,
+// allowing recovery from stream interruptions without losing progress.
+
+using A2A;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var a2aAgentHost = Environment.GetEnvironmentVariable("A2A_AGENT_HOST") ?? throw new InvalidOperationException("A2A_AGENT_HOST is not set.");
+
+// Initialize an A2ACardResolver to get an A2A agent card.
+A2ACardResolver agentCardResolver = new(new Uri(a2aAgentHost));
+
+// Get the agent card
+AgentCard agentCard = await agentCardResolver.GetAgentCardAsync();
+
+// Create an instance of the AIAgent for an existing A2A agent specified by the agent card.
+AIAgent agent = agentCard.AsAIAgent();
+
+AgentSession session = await agent.CreateSessionAsync();
+
+ResponseContinuationToken? continuationToken = null;
+
+await foreach (var update in agent.RunStreamingAsync("Conduct a comprehensive analysis of quantum computing applications in cryptography, including recent breakthroughs, implementation challenges, and future roadmap. Please include diagrams and visual representations to illustrate complex concepts.", session))
+{
+    // Saving the continuation token to be able to reconnect to the same response stream later.
+    // Note: Continuation tokens are only returned for long-running tasks. If the underlying A2A agent
+    // returns a message instead of a task, the continuation token will not be initialized.
+    // A2A agents do not support stream resumption from a specific point in the stream,
+    // but only reconnection to obtain the same response stream from the beginning.
+    // So, A2A agents will return an initialized continuation token in the first update
+    // representing the beginning of the stream, and it will be null in all subsequent updates.
+    if (update.ContinuationToken is { } token)
+    {
+        continuationToken = token;
+    }
+
+    // Imitating stream interruption
+    break;
+}
+
+// Reconnect to the same response stream using the continuation token obtained from the previous run.
+// As a first update, the agent will return an update representing the current state of the response at the moment of calling
+// RunStreamingAsync with the same continuation token, followed by other updates until the end of the stream is reached.
+if (continuationToken is not null)
+{
+    await foreach (var update in agent.RunStreamingAsync(session, options: new() { ContinuationToken = continuationToken }))
+    {
+        if (!string.IsNullOrEmpty(update.Text))
+        {
+            Console.WriteLine(update.Text);
+        }
+    }
+}

dotnet/samples/02-agents/A2A/A2AAgent_StreamReconnection/README.md

@@ -0,0 +1,29 @@
+# A2A Agent Stream Reconnection
+
+This sample demonstrates how to reconnect to an A2A agent's streaming response using continuation tokens, allowing recovery from stream interruptions without losing progress.
+
+The sample:
+
+- Connects to an A2A agent server specified in the `A2A_AGENT_HOST` environment variable
+- Sends a request to the agent and begins streaming the response
+- Captures a continuation token from the stream for later reconnection
+- Simulates a stream interruption by breaking out of the streaming loop
+- Reconnects to the same response stream using the captured continuation token
+- Displays the response received after reconnection
+
+This pattern is useful when network interruptions or other failures may disrupt an ongoing streaming response, and you need to recover and continue processing.
+
+> **Note:** Continuation tokens are only available when the underlying A2A agent returns a task. If the agent returns a message instead, the continuation token will not be initialized and stream reconnection is not applicable.
+
+# Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10.0 SDK or later
+- An A2A agent server running and accessible via HTTP
+
+Set the following environment variable:
+
+```powershell
+$env:A2A_AGENT_HOST="http://localhost:5000"  # Replace with your A2A agent server host
+```

dotnet/samples/02-agents/A2A/README.md

@@ -3,7 +3,7 @@
 These samples demonstrate how to work with Agent-to-Agent (A2A) specific features in the Agent Framework.
 
 For other samples that demonstrate how to use AIAgent instances,
-see the [Getting Started With Agents](../../02-agents/Agents/README.md) samples.
+see the [Getting Started With Agents](../Agents/README.md) samples.
 
 ## Prerequisites
 
@@ -15,6 +15,8 @@ See the README.md for each sample for the prerequisites for that sample.
 |---|---|
 |[A2A Agent As Function Tools](./A2AAgent_AsFunctionTools/)|This sample demonstrates how to represent an A2A agent as a set of function tools, where each function tool corresponds to a skill of the A2A agent, and register these function tools with another AI agent so it can leverage the A2A agent's skills.|
 |[A2A Agent Polling For Task Completion](./A2AAgent_PollingForTaskCompletion/)|This sample demonstrates how to poll for long-running task completion using continuation tokens with an A2A agent.|
+|[A2A Agent Stream Reconnection](./A2AAgent_StreamReconnection/)|This sample demonstrates how to reconnect to an A2A agent's streaming response using continuation tokens, allowing recovery from stream interruptions.|
+|[A2A Agent Protocol Selection](./A2AAgent_ProtocolSelection/)|This sample demonstrates how to select the A2A protocol binding (HTTP+JSON vs JSON-RPC) when creating an AIAgent from an A2A agent card using A2AClientOptions.|
 
 ## Running the samples from the console
 

dotnet/samples/02-agents/AGUI/README.md

@@ -15,7 +15,7 @@ All samples require the following environment variables:
 
 ```bash
 export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
 ```
 
 For the client samples, you can optionally set:

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Client/Program.cs

@@ -70,7 +70,7 @@ while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringCompa
 
                         if (approvalRequest.AdditionalProperties != null)
                         {
-                            approvalResponse.AdditionalProperties = new AdditionalPropertiesDictionary();
+                            approvalResponse.AdditionalProperties = [];
                             foreach (var kvp in approvalRequest.AdditionalProperties)
                             {
                                 approvalResponse.AdditionalProperties[kvp.Key] = kvp.Value;

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Client/ServerFunctionApprovalClientAgent.cs

@@ -131,9 +131,9 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                     transformedContents ??= CopyContentsUpToIndex(message.Contents, contentIndex);
                     approvalCalls.Remove(functionResult.CallId);
                 }
-                else if (transformedContents != null)
+                else
                 {
-                    transformedContents.Add(content);
+                    transformedContents?.Add(content);
                 }
             }
 
@@ -155,10 +155,10 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                 result ??= CopyMessagesUpToIndex(messages, messageIndex);
                 result.Add(newMessage);
             }
-            else if (result != null)
+            else
             {
                 // We're already copying messages, so copy this unchanged message too
-                result.Add(message);
+                result?.Add(message);
             }
             // If result is null, we haven't made any changes yet, so keep processing
         }

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Server/ServerFunctionApprovalServerAgent.cs

@@ -57,16 +57,10 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
             throw new InvalidOperationException("Invalid request_approval tool call");
         }
 
-        var request = toolCall.Arguments.TryGetValue("request", out var reqObj) &&
+        var request = (toolCall.Arguments.TryGetValue("request", out var reqObj) &&
             reqObj is JsonElement argsElement &&
             argsElement.Deserialize(jsonSerializerOptions.GetTypeInfo(typeof(ApprovalRequest))) is ApprovalRequest approvalRequest &&
-            approvalRequest != null ? approvalRequest : null;
-
-        if (request == null)
-        {
-            throw new InvalidOperationException("Failed to deserialize approval request from tool call");
-        }
-
+            approvalRequest != null ? approvalRequest : null) ?? throw new InvalidOperationException("Failed to deserialize approval request from tool call");
         return new ToolApprovalRequestContent(
             requestId: request.ApprovalId,
             new FunctionCallContent(
@@ -77,17 +71,11 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
 
     private static ToolApprovalResponseContent ConvertToolResultToApprovalResponse(FunctionResultContent result, ToolApprovalRequestContent approval, JsonSerializerOptions jsonSerializerOptions)
     {
-        var approvalResponse = result.Result is JsonElement je ?
+        var approvalResponse = (result.Result is JsonElement je ?
             (ApprovalResponse?)je.Deserialize(jsonSerializerOptions.GetTypeInfo(typeof(ApprovalResponse))) :
             result.Result is string str ?
                 (ApprovalResponse?)JsonSerializer.Deserialize(str, jsonSerializerOptions.GetTypeInfo(typeof(ApprovalResponse))) :
-                result.Result as ApprovalResponse;
-
-        if (approvalResponse == null)
-        {
-            throw new InvalidOperationException("Failed to deserialize approval response from tool result");
-        }
-
+                result.Result as ApprovalResponse) ?? throw new InvalidOperationException("Failed to deserialize approval response from tool result");
         return approval.CreateResponse(approvalResponse.Approved);
     }
 #pragma warning restore MEAI001
@@ -121,7 +109,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
         // Track approval ID to original call ID mapping
         _ = new Dictionary<string, string>();
 #pragma warning disable MEAI001 // Type is for evaluation purposes only and is subject to change or removal in future updates. Suppress this diagnostic to proceed.
-        Dictionary<string, ToolApprovalRequestContent> trackedRequestApprovalToolCalls = new(); // Remote approvals
+        Dictionary<string, ToolApprovalRequestContent> trackedRequestApprovalToolCalls = []; // Remote approvals
         for (int messageIndex = 0; messageIndex < messages.Count; messageIndex++)
         {
             var message = messages[messageIndex];
@@ -146,7 +134,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
                     });
                 }
                 else if (content is FunctionResultContent toolResult &&
-                    trackedRequestApprovalToolCalls.TryGetValue(toolResult.CallId, out var approval) == true)
+                    trackedRequestApprovalToolCalls.TryGetValue(toolResult.CallId, out var approval))
                 {
                     result ??= CopyMessagesUpToIndex(messages, messageIndex);
                     transformedContents ??= CopyContentsUpToIndex(message.Contents, j);
@@ -161,9 +149,9 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
                         AdditionalProperties = message.AdditionalProperties
                     });
                 }
-                else if (result != null)
+                else
                 {
-                    result.Add(message);
+                    result?.Add(message);
                 }
             }
         }

dotnet/samples/02-agents/AGUI/Step05_StateManagement/Client/StatefulAgent.cs

@@ -72,10 +72,9 @@ internal sealed class StatefulAgent<TState> : DelegatingAIAgent
                 if (content is DataContent dataContent && dataContent.MediaType == "application/json")
                 {
                     // Deserialize the state
-                    TState? newState = JsonSerializer.Deserialize(
+                    if (JsonSerializer.Deserialize(
                         dataContent.Data.Span,
-                        this._jsonSerializerOptions.GetTypeInfo(typeof(TState))) as TState;
-                    if (newState != null)
+                        this._jsonSerializerOptions.GetTypeInfo(typeof(TState))) is TState newState)
                     {
                         this.State = newState;
                     }

dotnet/samples/02-agents/AgentOpenTelemetry/Program.cs

@@ -18,11 +18,12 @@ using OpenTelemetry.Trace;
 
 #region Setup Telemetry
 
+// Source name for this sample's custom ActivitySource and Meter; other instrumentation uses their own sources/categories.
 const string SourceName = "OpenTelemetryAspire.ConsoleApp";
 const string ServiceName = "AgentOpenTelemetry";
 
 // Configure OpenTelemetry for Aspire dashboard
-var otlpEndpoint = Environment.GetEnvironmentVariable("OTEL_EXPORTER_OTLP_ENDPOINT") ?? "http://localhost:4318";
+var otlpEndpoint = Environment.GetEnvironmentVariable("OTEL_EXPORTER_OTLP_ENDPOINT") ?? "http://localhost:4317";
 
 var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
 
@@ -40,7 +41,6 @@ var resource = ResourceBuilder.CreateDefault()
 var tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
     .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"))
     .AddSource(SourceName) // Our custom activity source
-    .AddSource("*Microsoft.Agents.AI") // Agent Framework telemetry
     .AddHttpClientInstrumentation() // Capture HTTP calls to OpenAI
     .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint));
 
@@ -54,8 +54,7 @@ using var tracerProvider = tracerProviderBuilder.Build();
 // Setup metrics with resource and instrument name filtering
 using var meterProvider = Sdk.CreateMeterProviderBuilder()
     .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"))
-    .AddMeter(SourceName) // Our custom meter
-    .AddMeter("*Microsoft.Agents.AI") // Agent Framework metrics
+    .AddMeter(SourceName) // Our custom meter source
     .AddHttpClientInstrumentation() // HTTP client metrics
     .AddRuntimeInstrumentation() // .NET runtime metrics
     .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint))
@@ -98,7 +97,7 @@ Console.WriteLine("""
     """);
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT environment variable is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Log application startup
 appLogger.LogInformation("OpenTelemetry Aspire Demo application started");
@@ -128,7 +127,7 @@ var agent = new ChatClientAgent(instrumentedChatClient,
     instructions: "You are a helpful assistant that provides concise and informative responses.",
     tools: [AIFunctionFactory.Create(GetWeatherAsync)])
     .AsBuilder()
-    .UseOpenTelemetry(SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
+    .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
     .Build();
 
 var session = await agent.CreateSessionAsync();

dotnet/samples/02-agents/AgentOpenTelemetry/README.md

@@ -34,7 +34,7 @@ graph TD
 Set the following environment variables:
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
 
 **Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource.

dotnet/samples/02-agents/AgentProviders/Agent_With_Anthropic/README.md

@@ -5,8 +5,8 @@ This sample demonstrates how to create an AIAgent using Anthropic Claude models
 The sample supports three deployment scenarios:
 
 1. **Anthropic Public API** - Direct connection to Anthropic's public API
-2. **Azure Foundry with API Key** - Anthropic models deployed through Azure Foundry using API key authentication
-3. **Azure Foundry with Azure CLI** - Anthropic models deployed through Azure Foundry using Azure CLI credentials
+2. **Microsoft Foundry with API Key** - Anthropic models deployed through Microsoft Foundry using API key authentication
+3. **Microsoft Foundry with Azure CLI** - Anthropic models deployed through Microsoft Foundry using Azure CLI credentials
 
 ## Prerequisites
 
@@ -25,29 +25,29 @@ $env:ANTHROPIC_API_KEY="your-anthropic-api-key"  # Replace with your Anthropic A
 $env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
 ```
 
-### For Azure Foundry with API Key
+### For Microsoft Foundry with API Key
 
-- Azure Foundry service endpoint and deployment configured
+- Microsoft Foundry service endpoint and deployment configured
 - Anthropic API key
 
 Set the following environment variables:
 
 ```powershell
-$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
+$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
 $env:ANTHROPIC_API_KEY="your-anthropic-api-key"  # Replace with your Anthropic API key
 $env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
 ```
 
-### For Azure Foundry with Azure CLI
+### For Microsoft Foundry with Azure CLI
 
-- Azure Foundry service endpoint and deployment configured
+- Microsoft Foundry service endpoint and deployment configured
 - Azure CLI installed and authenticated (for Azure credential authentication)
 
 Set the following environment variables:
 
 ```powershell
-$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
+$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
 $env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
 ```
 
-**Note**: When using Azure Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: When using Microsoft Foundry with Azure CLI, make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/Program.cs

@@ -2,14 +2,14 @@
 
 #pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions
 
-// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.
+// This sample shows how to create and use a simple AI agent with Microsoft Foundry Agents as the backend.
 
 using Azure.AI.Agents.Persistent;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 const string JokerName = "Joker";
 const string JokerInstructions = "You are good at telling jokes.";

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/README.md

@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
 Before you begin, ensure you have the following prerequisites:
 
 - .NET 10 SDK or later
-- Azure Foundry service endpoint and deployment configured
+- Microsoft Foundry service endpoint and deployment configured
 - Azure CLI installed and authenticated (for Azure credential authentication)
 
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
 
 Set the following environment variables:
 
 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj

@@ -15,7 +15,7 @@
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Program.cs

@@ -1,28 +1,29 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to create and use a AI agents with Azure Foundry Agents as the backend.
+// This sample shows how to create and use AI agents with Microsoft Foundry Agents as the backend.
 
 using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Foundry;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 const string JokerName = "JokerAgent";
 
-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// Get a client to create/retrieve/delete server side agents with Microsoft Foundry Agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
 
 // Define the agent you want to create. (Prompt Agent in this case)
-var agentVersionCreationOptions = new AgentVersionCreationOptions(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
+var agentVersionCreationOptions = new ProjectsAgentVersionCreationOptions(new DeclarativeAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
 // Azure.AI.Agents SDK creates and manages agent by name and versions.
 // You can create a server side agent version with the Azure.AI.Agents SDK client below.
-var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options: agentVersionCreationOptions);
+var createdAgentVersion = aiProjectClient.AgentAdministrationClient.CreateAgentVersion(agentName: JokerName, options: agentVersionCreationOptions);
 
 // Note:
 //      agentVersion.Id = "<agentName>:<versionNumber>",
@@ -30,14 +31,18 @@ var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: J
 //      agentVersion.Name = <agentName>
 
 // You can use an AIAgent with an already created server side agent version.
-AIAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
+FoundryAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
 
 // You can also create another AIAgent version by providing the same name with a different definition.
-AIAgent newJokerAgent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: "You are extremely hilarious at telling jokes.");
+ProjectsAgentVersion newJokerAgentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
+    JokerName,
+    new ProjectsAgentVersionCreationOptions(new DeclarativeAgentDefinition(model: deploymentName) { Instructions = "You are extremely hilarious at telling jokes." }));
+FoundryAgent newJokerAgent = aiProjectClient.AsAIAgent(newJokerAgentVersion);
 
 // You can also get the AIAgent latest version just providing its name.
-AIAgent jokerAgentLatest = await aiProjectClient.GetAIAgentAsync(name: JokerName);
-var latestAgentVersion = jokerAgentLatest.GetService<AgentVersion>()!;
+ProjectsAgentRecord jokerAgentRecord = await aiProjectClient.AgentAdministrationClient.GetAgentAsync(JokerName);
+FoundryAgent jokerAgentLatest = aiProjectClient.AsAIAgent(jokerAgentRecord);
+ProjectsAgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();
 
 // The AIAgent version can be accessed via the GetService method.
 Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
@@ -50,4 +55,4 @@ Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate
 Console.WriteLine(await jokerAgentLatest.RunAsync("Now tell me a joke about a cat and a dog using last joke as the anchor.", session));
 
 // Cleanup by agent name removes both agent versions created.
-aiProjectClient.Agents.DeleteAgent(existingJokerAgent.Name);
+aiProjectClient.AgentAdministrationClient.DeleteAgent(existingJokerAgent.Name);

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/README.md

@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
 Before you begin, ensure you have the following prerequisites:
 
 - .NET 10 SDK or later
-- Azure Foundry service endpoint and deployment configured
+- Microsoft Foundry service endpoint and deployment configured
 - Azure CLI installed and authenticated (for Azure credential authentication)
 
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
 
 Set the following environment variables:
 
 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/Program.cs

@@ -1,7 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
-// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Azure AI Foundry resource.
+// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.
+// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Microsoft Foundry resource.
 // Note: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.
 
 using System.ClientModel;
@@ -15,7 +15,7 @@ var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? th
 var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
 var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "Phi-4-mini-instruct";
 
-// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Azure Foundry.
+// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Microsoft Foundry.
 var clientOptions = new OpenAIClientOptions() { Endpoint = new Uri(endpoint) };
 
 // Create the OpenAI client with either an API key or Azure CLI credential.

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/README.md

@@ -1,8 +1,8 @@
 ## Overview
 
-This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
+This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.
 
-You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Azure AI Foundry.
+You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Microsoft Foundry.
 
 **Note**: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.
 
@@ -11,19 +11,19 @@ You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI o
 Before you begin, ensure you have the following prerequisites:
 
 - .NET 10 SDK or later
-- Azure AI Foundry resource
-- A model deployment in your Azure AI Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
+- Microsoft Foundry resource
+- A model deployment in your Microsoft Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
 so if you want to use a different model, ensure that you set your `AZURE_AI_MODEL_DEPLOYMENT_NAME` environment
 variable to the name of your deployed model.
-- An API key or role based authentication to access the Azure AI Foundry resource
+- An API key or role based authentication to access the Microsoft Foundry resource
 
 See [here](https://learn.microsoft.com/en-us/azure/ai-foundry/quickstarts/get-started-code?tabs=csharp) for more info on setting up these prerequisites
 
 Set the following environment variables:
 
 ```powershell
-# Replace with your Azure AI Foundry resource endpoint
-# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Azure Foundry models.
+# Replace with your Microsoft Foundry resource endpoint
+# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Microsoft Foundry models.
 $env:AZURE_OPENAI_ENDPOINT="https://ai-foundry-<myresourcename>.services.ai.azure.com/openai/v1/"
 
 # Optional, defaults to using Azure CLI for authentication if not provided

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Program.cs

@@ -8,7 +8,7 @@ using Microsoft.Agents.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion/README.md

@@ -12,5 +12,5 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureOpenAIResponses/Program.cs

@@ -9,7 +9,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Responses;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureOpenAIResponses/README.md

@@ -12,5 +12,5 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/AgentProviders/Agent_With_GitHubCopilot/Program.cs

@@ -12,7 +12,9 @@ static Task<PermissionRequestResult> PromptPermission(PermissionRequest request,
     Console.Write("Approve? (y/n): ");
 
     string? input = Console.ReadLine()?.Trim().ToUpperInvariant();
-    string kind = input is "Y" or "YES" ? "approved" : "denied-interactively-by-user";
+    PermissionRequestResultKind kind = input is "Y" or "YES"
+                                     ? PermissionRequestResultKind.Approved
+                                     : PermissionRequestResultKind.Rejected;
 
     return Task.FromResult(new PermissionRequestResult { Kind = kind });
 }

dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIAssistants/Program.cs

@@ -1,41 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and use a simple AI agent with OpenAI Assistants as the backend.
-
-// WARNING: The Assistants API is deprecated and will be shut down.
-// For more information see the OpenAI documentation: https://platform.openai.com/docs/assistants/migration
-
-#pragma warning disable CS0618 // Type or member is obsolete - OpenAI Assistants API is deprecated but still used in this sample
-
-using Microsoft.Agents.AI;
-using OpenAI;
-using OpenAI.Assistants;
-
-var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
-
-const string JokerName = "Joker";
-const string JokerInstructions = "You are good at telling jokes.";
-
-// Get a client to create/retrieve server side agents with.
-var assistantClient = new OpenAIClient(apiKey).GetAssistantClient();
-
-// You can create a server side assistant with the OpenAI SDK.
-var createResult = await assistantClient.CreateAssistantAsync(model, new() { Name = JokerName, Instructions = JokerInstructions });
-
-// You can retrieve an already created server side assistant as an AIAgent.
-AIAgent agent1 = await assistantClient.GetAIAgentAsync(createResult.Value.Id);
-
-// You can also create a server side assistant and return it as an AIAgent directly.
-AIAgent agent2 = await assistantClient.CreateAIAgentAsync(
-    model: model,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can invoke the agent like any other AIAgent.
-AgentSession session = await agent1.CreateSessionAsync();
-Console.WriteLine(await agent1.RunAsync("Tell me a joke about a pirate.", session));
-
-// Cleanup for sample purposes.
-await assistantClient.DeleteAssistantAsync(agent1.Id);
-await assistantClient.DeleteAssistantAsync(agent2.Id);

dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIAssistants/README.md

@@ -1,16 +0,0 @@
-# Prerequisites
-
-WARNING: The Assistants API is deprecated and will be shut down.
-For more information see the OpenAI documentation: https://platform.openai.com/docs/assistants/migration
-
-Before you begin, ensure you have the following prerequisites:
-
-- .NET 10 SDK or later
-- OpenAI API key
-
-Set the following environment variables:
-
-```powershell
-$env:OPENAI_API_KEY="*****" # Replace with your OpenAI API key
-$env:OPENAI_CHAT_MODEL_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
-```

dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIChatCompletion/Program.cs

@@ -8,7 +8,7 @@ using OpenAI;
 using OpenAI.Chat;
 
 var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 AIAgent agent = new OpenAIClient(
     apiKey)

dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIChatCompletion/README.md

@@ -9,5 +9,5 @@ Set the following environment variables:
 
 ```powershell
 $env:OPENAI_API_KEY="*****" # Replace with your OpenAI api key
-$env:OPENAI_CHAT_MODEL_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:OPENAI_CHAT_MODEL_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIResponses/Program.cs

@@ -7,7 +7,7 @@ using OpenAI;
 using OpenAI.Responses;
 
 var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 AIAgent agent = new OpenAIClient(
     apiKey)

dotnet/samples/02-agents/AgentProviders/Agent_With_OpenAIResponses/README.md

@@ -9,5 +9,5 @@ Set the following environment variables:
 
 ```powershell
 $env:OPENAI_API_KEY="*****" # Replace with your OpenAI api key
-$env:OPENAI_CHAT_MODEL_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:OPENAI_CHAT_MODEL_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/AgentProviders/README.md

@@ -18,14 +18,13 @@ See the README.md for each sample for the prerequisites for that sample.
 |[Creating an AIAgent with Anthropic](./Agent_With_Anthropic/)|This sample demonstrates how to create an AIAgent using Anthropic Claude models as the underlying inference service|
 |[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
 |[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
-|[Creating an AIAgent with AzureFoundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Azure Foundry to create an AIAgent|
+|[Creating an AIAgent with Foundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Microsoft Foundry to create an AIAgent|
 |[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
 |[Creating an AIAgent with Azure OpenAI Responses](./Agent_With_AzureOpenAIResponses/)|This sample demonstrates how to create an AIAgent using Azure OpenAI Responses as the underlying inference service|
 |[Creating an AIAgent with a custom implementation](./Agent_With_CustomImplementation/)|This sample demonstrates how to create an AIAgent with a custom implementation|
 |[Creating an AIAgent with GitHub Copilot](./Agent_With_GitHubCopilot/)|This sample demonstrates how to create an AIAgent using GitHub Copilot SDK as the underlying inference service|
 |[Creating an AIAgent with Ollama](./Agent_With_Ollama/)|This sample demonstrates how to create an AIAgent using Ollama as the underlying inference service|
 |[Creating an AIAgent with ONNX](./Agent_With_ONNX/)|This sample demonstrates how to create an AIAgent using ONNX as the underlying inference service|
-|[Creating an AIAgent with OpenAI Assistants](./Agent_With_OpenAIAssistants/)|This sample demonstrates how to create an AIAgent using OpenAI Assistants as the underlying inference service.</br>WARNING: The Assistants API is deprecated and will be shut down. For more information see the OpenAI documentation: https://platform.openai.com/docs/assistants/migration|
 |[Creating an AIAgent with OpenAI ChatCompletion](./Agent_With_OpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using OpenAI ChatCompletion as the underlying inference service|
 |[Creating an AIAgent with OpenAI Responses](./Agent_With_OpenAIResponses/)|This sample demonstrates how to create an AIAgent using OpenAI Responses as the underlying inference service|
 

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/Program.cs

@@ -1,50 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to use Agent Skills with a ChatClientAgent.
-// Agent Skills are modular packages of instructions and resources that extend an agent's capabilities.
-// Skills follow the progressive disclosure pattern: advertise -> load -> read resources.
-//
-// This sample includes the expense-report skill:
-//   - Policy-based expense filing with references and assets
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Responses;
-
-// --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// --- Skills Provider ---
-// Discovers skills from the 'skills' directory and makes them available to the agent
-var skillsProvider = new FileAgentSkillsProvider(skillPath: Path.Combine(AppContext.BaseDirectory, "skills"));
-
-// --- Agent Setup ---
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
-    .AsAIAgent(new ChatClientAgentOptions
-    {
-        Name = "SkillsAgent",
-        ChatOptions = new()
-        {
-            Instructions = "You are a helpful assistant.",
-        },
-        AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
-
-// --- Example 1: Expense policy question (loads FAQ resource) ---
-Console.WriteLine("Example 1: Checking expense policy FAQ");
-Console.WriteLine("---------------------------------------");
-AgentResponse response1 = await agent.RunAsync("Are tips reimbursable? I left a 25% tip on a taxi ride and want to know if that's covered.");
-Console.WriteLine($"Agent: {response1.Text}\n");
-
-// --- Example 2: Filing an expense report (multi-turn with template asset) ---
-Console.WriteLine("Example 2: Filing an expense report");
-Console.WriteLine("---------------------------------------");
-AgentSession session = await agent.CreateSessionAsync();
-AgentResponse response2 = await agent.RunAsync("I had 3 client dinners and a $1,200 flight last week. Return a draft expense report and ask about any missing details.",
-    session);
-Console.WriteLine($"Agent: {response2.Text}\n");

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/README.md

@@ -1,63 +0,0 @@
-# Agent Skills Sample
-
-This sample demonstrates how to use **Agent Skills** with a `ChatClientAgent` in the Microsoft Agent Framework.
-
-## What are Agent Skills?
-
-Agent Skills are modular packages of instructions and resources that enable AI agents to perform specialized tasks. They follow the [Agent Skills specification](https://agentskills.io/) and implement the progressive disclosure pattern:
-
-1. **Advertise**: Skills are advertised with name + description (~100 tokens per skill)
-2. **Load**: Full instructions are loaded on-demand via `load_skill` tool
-3. **Resources**: References and other files loaded via `read_skill_resource` tool
-
-## Skills Included
-
-### expense-report
-Policy-based expense filing with spending limits, receipt requirements, and approval workflows.
-- `references/POLICY_FAQ.md` — Detailed expense policy Q&A
-- `assets/expense-report-template.md` — Submission template
-
-## Project Structure
-
-```
-Agent_Step01_BasicSkills/
-├── Program.cs
-├── Agent_Step01_BasicSkills.csproj
-└── skills/
-    └── expense-report/
-        ├── SKILL.md
-        ├── references/
-        │   └── POLICY_FAQ.md
-        └── assets/
-            └── expense-report-template.md
-```
-
-## Running the Sample
-
-### Prerequisites
-- .NET 10.0 SDK
-- Azure OpenAI endpoint with a deployed model
-
-### Setup
-1. Set environment variables:
-   ```bash
-   export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
-   export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
-   ```
-
-2. Run the sample:
-   ```bash
-   dotnet run
-   ```
-
-### Examples
-
-The sample runs two examples:
-
-1. **Expense policy FAQ** — Asks about tip reimbursement; the agent loads the expense-report skill and reads the FAQ resource
-2. **Filing an expense report** — Multi-turn conversation to draft an expense report using the template asset
-
-## Learn More
-
-- [Agent Skills Specification](https://agentskills.io/)
-- [Microsoft Agent Framework Documentation](../../../../../docs/)

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/skills/expense-report/SKILL.md

@@ -1,40 +0,0 @@
----
-name: expense-report
-description: File and validate employee expense reports according to Contoso company policy. Use when asked about expense submissions, reimbursement rules, receipt requirements, spending limits, or expense categories.
-metadata:
-  author: contoso-finance
-  version: "2.1"
----
-
-# Expense Report
-
-## Categories and Limits
-
-| Category | Limit | Receipt | Approval |
-|---|---|---|---|
-| Meals — solo | $50/day | >$25 | No |
-| Meals — team/client | $75/person | Always | Manager if >$200 total |
-| Lodging | $250/night | Always | Manager if >3 nights |
-| Ground transport | $100/day | >$15 | No |
-| Airfare | Economy | Always | Manager; VP if >$1,500 |
-| Conference/training | $2,000/event | Always | Manager + L&D |
-| Office supplies | $100 | Yes | No |
-| Software/subscriptions | $50/month | Yes | Manager if >$200/year |
-
-## Filing Process
-
-1. Collect receipts — must show vendor, date, amount, payment method.
-2. Categorize per table above.
-3. Use template: [assets/expense-report-template.md](assets/expense-report-template.md).
-4. For client/team meals: list attendee names and business purpose.
-5. Submit — auto-approved if <$500; manager if $500–$2,000; VP if >$2,000.
-6. Reimbursement: 10 business days via direct deposit.
-
-## Policy Rules
-
-- Submit within 30 days of transaction.
-- Alcohol is never reimbursable.
-- Foreign currency: convert to USD at transaction-date rate; note original currency and amount.
-- Mixed personal/business travel: only business portion reimbursable; provide comparison quotes.
-- Lost receipts (>$25): file Lost Receipt Affidavit from Finance. Max 2 per quarter.
-- For policy questions not covered above, consult the FAQ: [references/POLICY_FAQ.md](references/POLICY_FAQ.md). Answers should be based on what this document and the FAQ state.

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/skills/expense-report/assets/expense-report-template.md

@@ -1,5 +0,0 @@
-# Expense Report Template
-
-| Date | Category | Vendor | Description | Amount (USD) | Original Currency | Original Amount | Attendees | Business Purpose | Receipt Attached |
-|------|----------|--------|-------------|--------------|-------------------|-----------------|-----------|------------------|------------------|
-|      |          |        |             |              |                   |                 |           |                  | Yes or No        |

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/skills/expense-report/references/POLICY_FAQ.md

@@ -1,55 +0,0 @@
-# Expense Policy — Frequently Asked Questions
-
-## Meals
-
-**Q: Can I expense coffee or snacks during the workday?**
-A: Daily coffee/snacks under $10 are not reimbursable (considered personal). Coffee purchased during a client meeting or team working session is reimbursable as a team meal.
-
-**Q: What if a team dinner exceeds the per-person limit?**
-A: The $75/person limit applies as a guideline. Overages up to 20% are accepted with a written justification (e.g., "client dinner at venue chosen by client"). Overages beyond 20% require pre-approval from your VP.
-
-**Q: Do I need to list every attendee?**
-A: Yes. For client meals, list the client's name and company. For team meals, list all employee names. For groups over 10, you may attach a separate attendee list.
-
-## Travel
-
-**Q: Can I book a premium economy or business class flight?**
-A: Economy class is the standard. Premium economy is allowed for flights over 6 hours. Business class requires VP pre-approval and is generally reserved for flights over 10 hours or medical accommodation.
-
-**Q: What about ride-sharing (Uber/Lyft) vs. rental cars?**
-A: Use ride-sharing for trips under 30 miles round-trip. Rent a car for multi-day travel or when ride-sharing would exceed $100/day. Always choose the compact/standard category unless traveling with 3+ people.
-
-**Q: Are tips reimbursable?**
-A: Tips up to 20% are reimbursable for meals, taxi/ride-share, and hotel housekeeping. Tips above 20% require justification.
-
-## Lodging
-
-**Q: What if the $250/night limit isn't enough for the city I'm visiting?**
-A: For high-cost cities (New York, San Francisco, London, Tokyo, Sydney), the limit is automatically increased to $350/night. No additional approval is needed. For other locations where rates are unusually high (e.g., during a major conference), request a per-trip exception from your manager before booking.
-
-**Q: Can I stay with friends/family instead and get a per-diem?**
-A: No. Contoso reimburses actual lodging costs only, not per-diems.
-
-## Subscriptions and Software
-
-**Q: Can I expense a personal productivity tool?**
-A: Software must be directly related to your job function. Tools like IDE licenses, design software, or project management apps are reimbursable. General productivity apps (note-taking, personal calendar) are not, unless your manager confirms a business need in writing.
-
-**Q: What about annual subscriptions?**
-A: Annual subscriptions over $200 require manager approval before purchase. Submit the approval email with your expense report.
-
-## Receipts and Documentation
-
-**Q: My receipt is faded/damaged. What do I do?**
-A: Try to obtain a duplicate from the vendor. If not possible, submit a Lost Receipt Affidavit (available from the Finance SharePoint site). You're limited to 2 affidavits per quarter.
-
-**Q: Do I need a receipt for parking meters or tolls?**
-A: For amounts under $15, no receipt is required — just note the date, location, and amount. For $15 and above, a receipt or bank/credit card statement excerpt is required.
-
-## Approval and Reimbursement
-
-**Q: My manager is on leave. Who approves my report?**
-A: Expense reports can be approved by your skip-level manager or any manager designated as an alternate approver in the expense system.
-
-**Q: Can I submit expenses from a previous quarter?**
-A: The standard 30-day window applies. Expenses older than 30 days require a written explanation and VP approval. Expenses older than 90 days are not reimbursable except in extraordinary circumstances (extended leave, medical emergency) with CFO approval.

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj

@@ -14,6 +14,10 @@
     <PackageReference Include="Azure.Identity" />
   </ItemGroup>
 
+  <ItemGroup>
+    <Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
+  </ItemGroup>
+
   <ItemGroup>
     <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
   </ItemGroup>

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Program.cs

@@ -0,0 +1,48 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use file-based Agent Skills with a ChatClientAgent.
+// Skills are discovered from SKILL.md files on disk and follow the progressive disclosure pattern:
+// 1. Advertise — skill names and descriptions in the system prompt
+// 2. Load — full instructions loaded on demand via load_skill tool
+// 3. Read resources — reference files read via read_skill_resource tool
+// 4. Run scripts — scripts executed via run_skill_script tool with a subprocess executor
+//
+// This sample uses a unit-converter skill that converts between miles, kilometers, pounds, and kilograms.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- Skills Provider ---
+// Discovers skills from the 'skills' directory containing SKILL.md files.
+// The script runner runs file-based scripts (e.g. Python) as local subprocesses.
+var skillsProvider = new AgentSkillsProvider(
+    Path.Combine(AppContext.BaseDirectory, "skills"),
+    SubprocessScriptRunner.RunAsync);
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Unit conversion ---
+Console.WriteLine("Converting units with file-based skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/README.md

@@ -0,0 +1,51 @@
+# File-Based Agent Skills Sample
+
+This sample demonstrates how to use **file-based Agent Skills** with a `ChatClientAgent`.
+
+## What it demonstrates
+
+- Discovering skills from `SKILL.md` files on disk via `AgentFileSkillsSource`
+- The progressive disclosure pattern: advertise → load → read resources → run scripts
+- Using the `AgentSkillsProvider` constructor with a skill directory path and script runner
+- Running file-based scripts (Python) via a subprocess-based executor
+
+## Skills Included
+
+### unit-converter
+
+Converts between common units (miles↔km, pounds↔kg) using a multiplication factor.
+
+- `references/conversion-table.md` — Conversion factor table
+- `scripts/convert.py` — Python script that performs the conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+- Python 3 installed and available as `python3` on your PATH
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with file-based skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/skills/unit-converter/SKILL.md

@@ -0,0 +1,11 @@
+---
+name: unit-converter
+description: Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.
+---
+
+## Usage
+
+When the user requests a unit conversion:
+1. First, review `references/conversion-table.md` to find the correct factor
+2. Run the `scripts/convert.py` script with `--value <number> --factor <factor>` (e.g. `--value 26.2 --factor 1.60934`)
+3. Present the converted value clearly with both units

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/skills/unit-converter/references/conversion-table.md

@@ -0,0 +1,10 @@
+# Conversion Tables
+
+Formula: **result = value × factor**
+
+| From        | To          | Factor   |
+|-------------|-------------|----------|
+| miles       | kilometers  | 1.60934  |
+| kilometers  | miles       | 0.621371 |
+| pounds      | kilograms   | 0.453592 |
+| kilograms   | pounds      | 2.20462  |

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/skills/unit-converter/scripts/convert.py

@@ -0,0 +1,29 @@
+# Unit conversion script
+# Converts a value using a multiplication factor: result = value × factor
+#
+# Usage:
+#   python scripts/convert.py --value 26.2 --factor 1.60934
+#   python scripts/convert.py --value 75 --factor 2.20462
+
+import argparse
+import json
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Convert a value using a multiplication factor.",
+        epilog="Examples:\n"
+        "  python scripts/convert.py --value 26.2 --factor 1.60934\n"
+        "  python scripts/convert.py --value 75 --factor 2.20462",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--value", type=float, required=True, help="The numeric value to convert.")
+    parser.add_argument("--factor", type=float, required=True, help="The conversion factor from the table.")
+    args = parser.parse_args()
+
+    result = round(args.value * args.factor, 4)
+    print(json.dumps({"value": args.value, "factor": args.factor, "result": result}))
+
+
+if __name__ == "__main__":
+    main()

dotnet/samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj

@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAI001</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Program.cs

@@ -0,0 +1,90 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to define Agent Skills entirely in code using AgentInlineSkill.
+// No SKILL.md files are needed — skills, resources, and scripts are all defined programmatically.
+//
+// Three approaches are shown using a unit-converter skill:
+// 1. Static resources — inline content provided via AddResource
+// 2. Dynamic resources — computed at runtime via a factory delegate
+// 3. Code scripts — executable delegates the agent can invoke directly
+
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- Build the code-defined skill ---
+var unitConverterSkill = new AgentInlineSkill(
+    name: "unit-converter",
+    description: "Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.",
+    instructions: """
+        Use this skill when the user asks to convert between units.
+
+        1. Review the conversion-table resource to find the factor for the requested conversion.
+        2. Check the conversion-policy resource for rounding and formatting rules.
+        3. Use the convert script, passing the value and factor from the table.
+        """)
+    // 1. Static Resource: conversion tables
+    .AddResource(
+        "conversion-table",
+        """
+        # Conversion Tables
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | miles       | kilometers  | 1.60934  |
+        | kilometers  | miles       | 0.621371 |
+        | pounds      | kilograms   | 0.453592 |
+        | kilograms   | pounds      | 2.20462  |
+        """)
+    // 2. Dynamic Resource: conversion policy (computed at runtime)
+    .AddResource("conversion-policy", () =>
+    {
+        const int Precision = 4;
+        return $"""
+            # Conversion Policy
+
+            **Decimal places:** {Precision}
+            **Format:** Always show both the original and converted values with units
+            **Generated at:** {DateTime.UtcNow:O}
+            """;
+    })
+    // 3. Code Script: convert
+    .AddScript("convert", (double value, double factor) =>
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    });
+
+// --- Skills Provider ---
+var skillsProvider = new AgentSkillsProvider(unitConverterSkill);
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Unit conversion ---
+Console.WriteLine("Converting units with code-defined skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");

dotnet/samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/README.md

@@ -0,0 +1,52 @@
+# Code-Defined Agent Skills Sample
+
+This sample demonstrates how to define **Agent Skills entirely in code** using `AgentInlineSkill`.
+
+## What it demonstrates
+
+- Creating skills programmatically with `AgentInlineSkill` — no SKILL.md files needed
+- **Static resources** via `AddResource` with inline content
+- **Dynamic resources** via `AddResource` with a factory delegate (computed at runtime)
+- **Code scripts** via `AddScript` with a delegate handler
+- Using the `AgentSkillsProvider` constructor with inline skills
+
+## Skills Included
+
+### unit-converter (code-defined)
+
+Converts between common units using multiplication factors. Defined entirely in C# code:
+
+- `conversion-table` — Static resource with factor table
+- `conversion-policy` — Dynamic resource with formatting rules (generated at runtime)
+- `convert` — Script that performs `value × factor` conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with code-defined skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```

dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Agent_Step03_ClassBasedSkills.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -6,16 +6,16 @@
 
     <Nullable>enable</Nullable>
     <ImplicitUsings>enable</ImplicitUsings>
-    <NoWarn>$(NoWarn);IDE0059</NoWarn>
+    <NoWarn>$(NoWarn);MAAI001;IDE0051</NoWarn>
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.AI.OpenAI" />
     <PackageReference Include="Azure.Identity" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Program.cs

@@ -0,0 +1,111 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to define Agent Skills as C# classes using AgentClassSkill
+// with attributes for automatic script and resource discovery.
+
+using System.ComponentModel;
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- Class-Based Skill ---
+// Instantiate the skill class.
+var unitConverter = new UnitConverterSkill();
+
+// --- Skills Provider ---
+var skillsProvider = new AgentSkillsProvider(unitConverter);
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Unit conversion ---
+Console.WriteLine("Converting units with class-based skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");
+
+/// <summary>
+/// A unit-converter skill defined as a C# class using attributes for discovery.
+/// </summary>
+/// <remarks>
+/// Properties annotated with <see cref="AgentSkillResourceAttribute"/> are automatically
+/// discovered as skill resources, and methods annotated with <see cref="AgentSkillScriptAttribute"/>
+/// are automatically discovered as skill scripts. Alternatively,
+/// <see cref="AgentSkill.Resources"/> and <see cref="AgentSkill.Scripts"/> can be overridden.
+/// </remarks>
+internal sealed class UnitConverterSkill : AgentClassSkill<UnitConverterSkill>
+{
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; } = new(
+        "unit-converter",
+        "Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.");
+
+    /// <inheritdoc/>
+    protected override string Instructions => """
+        Use this skill when the user asks to convert between units.
+
+        1. Review the conversion-table resource to find the factor for the requested conversion.
+        2. Use the convert script, passing the value and factor from the table.
+        3. Present the result clearly with both units.
+        """;
+
+    /// <summary>
+    /// Gets the <see cref="JsonSerializerOptions"/> used to marshal parameters and return values
+    /// for scripts and resources.
+    /// </summary>
+    /// <remarks>
+    /// This override is not necessary for this sample, but can be used to provide custom
+    /// serialization options, for example a source-generated <c>JsonTypeInfoResolver</c>
+    /// for Native AOT compatibility.
+    /// </remarks>
+    protected override JsonSerializerOptions? SerializerOptions => null;
+
+    /// <summary>
+    /// A conversion table resource providing multiplication factors.
+    /// </summary>
+    [AgentSkillResource("conversion-table")]
+    [Description("Lookup table of multiplication factors for common unit conversions.")]
+    public string ConversionTable => """
+        # Conversion Tables
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | miles       | kilometers  | 1.60934  |
+        | kilometers  | miles       | 0.621371 |
+        | pounds      | kilograms   | 0.453592 |
+        | kilograms   | pounds      | 2.20462  |
+        """;
+
+    /// <summary>
+    /// Converts a value by the given factor.
+    /// </summary>
+    [AgentSkillScript("convert")]
+    [Description("Multiplies a value by a conversion factor and returns the result as JSON.")]
+    private static string ConvertUnits(double value, double factor)
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    }
+}

dotnet/samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/README.md

@@ -0,0 +1,53 @@
+# Class-Based Agent Skills Sample
+
+This sample demonstrates how to define **Agent Skills as C# classes** using `AgentClassSkill`
+with **attributes** for automatic script and resource discovery.
+
+## What it demonstrates
+
+- Creating skills as classes that extend `AgentClassSkill`
+- Using `[AgentSkillResource]` on properties to define resources
+- Using `[AgentSkillScript]` on methods to define scripts
+- Automatic discovery (no need to override `Resources`/`Scripts`)
+- Using the `AgentSkillsProvider` constructor with class-based skills
+- Overriding `SerializerOptions` for Native AOT compatibility
+
+## Skills Included
+
+### unit-converter (class-based)
+
+A `UnitConverterSkill` class that converts between common units. Defined in `Program.cs`:
+
+- `conversion-table` — Static resource with factor table
+- `convert` — Script that performs `value × factor` conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with class-based skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Agent_Step04_MixedSkills.csproj

@@ -0,0 +1,32 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAI001;IDE0051</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+  <!-- Copy skills directory to output -->
+  <ItemGroup>
+    <None Include="skills\**\*.*">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Program.cs

@@ -0,0 +1,150 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates an advanced scenario: combining multiple skill types in a single agent
+// using AgentSkillsProviderBuilder. The builder is designed for cases where the simple
+// AgentSkillsProvider constructors are insufficient — for example, when you need to mix skill
+// sources, apply filtering, or configure cross-cutting options in one place.
+//
+// Three different skill sources are registered here:
+// 1. File-based: unit-converter (miles↔km, pounds↔kg) from SKILL.md on disk
+// 2. Code-defined: volume-converter (gallons↔liters) using AgentInlineSkill
+// 3. Class-based: temperature-converter (°F↔°C↔K) using AgentClassSkill with attributes
+//
+// For simpler, single-source scenarios, see the earlier steps in this sample series
+// (e.g., Step01 for file-based, Step02 for code-defined, Step03 for class-based).
+
+using System.ComponentModel;
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- 1. Code-Defined Skill: volume-converter ---
+var volumeConverterSkill = new AgentInlineSkill(
+    name: "volume-converter",
+    description: "Convert between gallons and liters using a multiplication factor.",
+    instructions: """
+        Use this skill when the user asks to convert between gallons and liters.
+
+        1. Review the volume-conversion-table resource to find the correct factor.
+        2. Use the convert-volume script, passing the value and factor.
+        """)
+    .AddResource("volume-conversion-table",
+        """
+        # Volume Conversion Table
+
+        Formula: **result = value × factor**
+
+        | From    | To      | Factor  |
+        |---------|---------|---------|
+        | gallons | liters  | 3.78541 |
+        | liters  | gallons | 0.264172|
+        """)
+    .AddScript("convert-volume", (double value, double factor) =>
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    });
+
+// --- 2. Class-Based Skill: temperature-converter ---
+var temperatureConverter = new TemperatureConverterSkill();
+
+// --- 3. Build provider combining all three source types ---
+var skillsProvider = new AgentSkillsProviderBuilder()
+    .UseFileSkill(Path.Combine(AppContext.BaseDirectory, "skills"))    // File-based: unit-converter
+    .UseSkill(volumeConverterSkill)                                    // Code-defined: volume-converter
+    .UseSkill(temperatureConverter)                                     // Class-based: temperature-converter
+    .UseFileScriptRunner(SubprocessScriptRunner.RunAsync)
+    .Build();
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "MultiConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units, volumes, and temperatures.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Use all three skills ---
+Console.WriteLine("Converting with mixed skills (file + code + class)");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "I need three conversions: " +
+    "1) How many kilometers is a marathon (26.2 miles)? " +
+    "2) How many liters is a 5-gallon bucket? " +
+    "3) What is 98.6°F in Celsius?");
+
+Console.WriteLine($"Agent: {response.Text}");
+
+/// <summary>
+/// A temperature-converter skill defined as a C# class using attributes for discovery.
+/// </summary>
+/// <remarks>
+/// Properties annotated with <see cref="AgentSkillResourceAttribute"/> are automatically
+/// discovered as skill resources, and methods annotated with <see cref="AgentSkillScriptAttribute"/>
+/// are automatically discovered as skill scripts.
+/// </remarks>
+internal sealed class TemperatureConverterSkill : AgentClassSkill<TemperatureConverterSkill>
+{
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; } = new(
+        "temperature-converter",
+        "Convert between temperature scales (Fahrenheit, Celsius, Kelvin).");
+
+    /// <inheritdoc/>
+    protected override string Instructions => """
+        Use this skill when the user asks to convert temperatures.
+
+        1. Review the temperature-conversion-formulas resource for the correct formula.
+        2. Use the convert-temperature script, passing the value, source scale, and target scale.
+        3. Present the result clearly with both temperature scales.
+        """;
+
+    /// <summary>
+    /// A reference table of temperature conversion formulas.
+    /// </summary>
+    [AgentSkillResource("temperature-conversion-formulas")]
+    [Description("Formulas for converting between Fahrenheit, Celsius, and Kelvin.")]
+    public string ConversionFormulas => """
+        # Temperature Conversion Formulas
+
+        | From        | To          | Formula                   |
+        |-------------|-------------|---------------------------|
+        | Fahrenheit  | Celsius     | °C = (°F − 32) × 5/9     |
+        | Celsius     | Fahrenheit  | °F = (°C × 9/5) + 32     |
+        | Celsius     | Kelvin      | K = °C + 273.15           |
+        | Kelvin      | Celsius     | °C = K − 273.15           |
+        """;
+
+    /// <summary>
+    /// Converts a temperature value between scales.
+    /// </summary>
+    [AgentSkillScript("convert-temperature")]
+    [Description("Converts a temperature value from one scale to another.")]
+    private static string ConvertTemperature(double value, string from, string to)
+    {
+        double result = (from.ToUpperInvariant(), to.ToUpperInvariant()) switch
+        {
+            ("FAHRENHEIT", "CELSIUS") => Math.Round((value - 32) * 5.0 / 9.0, 2),
+            ("CELSIUS", "FAHRENHEIT") => Math.Round(value * 9.0 / 5.0 + 32, 2),
+            ("CELSIUS", "KELVIN") => Math.Round(value + 273.15, 2),
+            ("KELVIN", "CELSIUS") => Math.Round(value - 273.15, 2),
+            _ => throw new ArgumentException($"Unsupported conversion: {from} → {to}")
+        };
+
+        return JsonSerializer.Serialize(new { value, from, to, result });
+    }
+}

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/README.md

@@ -0,0 +1,67 @@
+# Mixed Agent Skills Sample (Advanced)
+
+This sample demonstrates an **advanced scenario**: combining multiple skill types in a single agent using `AgentSkillsProviderBuilder`.
+
+> **Tip:** For simpler, single-source scenarios, use the `AgentSkillsProvider` constructors directly — see [Step01](../Agent_Step01_FileBasedSkills/) (file-based), [Step02](../Agent_Step02_CodeDefinedSkills/) (code-defined), or [Step03](../Agent_Step03_ClassBasedSkills/) (class-based).
+
+## What it demonstrates
+
+- Combining file-based, code-defined, and class-based skills in one provider
+- Using `UseFileSkill` and `UseSkill` on the builder to register different skill types
+- Aggregating skills from all sources into a single provider with automatic deduplication
+
+## When to use `AgentSkillsProviderBuilder`
+
+The builder is intended for advanced scenarios where the simple `AgentSkillsProvider` constructors are insufficient:
+
+| Scenario | Builder method |
+|----------|---------------|
+| **Mixed skill types** — combine file-based, code-defined, and class-based skills | `UseFileSkill` + `UseSkill` / `UseSkills` |
+| **Multiple file script runners** — use different script runners for different file skill directories | `UseFileSkill` / `UseFileSkills` with per-source `scriptRunner` |
+| **Skill filtering** — include/exclude skills using a predicate | `UseFilter(predicate)` |
+
+## Skills Included
+
+### unit-converter (file-based)
+
+Discovered from `skills/unit-converter/SKILL.md` on disk. Converts miles↔km, pounds↔kg.
+
+### volume-converter (code-defined)
+
+Defined as `AgentInlineSkill` in `Program.cs`. Converts gallons↔liters.
+
+### temperature-converter (class-based)
+
+Defined as `TemperatureConverterSkill` class in `Program.cs`. Converts °F↔°C↔K.
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting with mixed skills (file + code + class)
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **5 gallons → 18.93 liters**
+3. **98.6°F → 37.0°C**
+```

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/skills/unit-converter/SKILL.md

@@ -0,0 +1,11 @@
+---
+name: unit-converter
+description: Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.
+---
+
+## Usage
+
+When the user requests a unit conversion:
+1. First, review `references/unit-conversion-table.md` to find the correct factor
+2. Run the `scripts/convert-units.py` script with `--value <number> --factor <factor>` (e.g. `--value 26.2 --factor 1.60934`)
+3. Present the converted value clearly with both units

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/skills/unit-converter/references/unit-conversion-table.md

@@ -0,0 +1,10 @@
+# Conversion Tables
+
+Formula: **result = value × factor**
+
+| From        | To          | Factor   |
+|-------------|-------------|----------|
+| miles       | kilometers  | 1.60934  |
+| kilometers  | miles       | 0.621371 |
+| pounds      | kilograms   | 0.453592 |
+| kilograms   | pounds      | 2.20462  |

dotnet/samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/skills/unit-converter/scripts/convert-units.py

@@ -0,0 +1,29 @@
+# Unit conversion script
+# Converts a value using a multiplication factor: result = value × factor
+#
+# Usage:
+#   python scripts/convert-units.py --value 26.2 --factor 1.60934
+#   python scripts/convert-units.py --value 75 --factor 2.20462
+
+import argparse
+import json
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Convert a value using a multiplication factor.",
+        epilog="Examples:\n"
+        "  python scripts/convert-units.py --value 26.2 --factor 1.60934\n"
+        "  python scripts/convert-units.py --value 75 --factor 2.20462",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--value", type=float, required=True, help="The numeric value to convert.")
+    parser.add_argument("--factor", type=float, required=True, help="The conversion factor from the table.")
+    args = parser.parse_args()
+
+    result = round(args.value * args.factor, 4)
+    print(json.dumps({"value": args.value, "factor": args.factor, "result": result}))
+
+
+if __name__ == "__main__":
+    main()

dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Agent_Step05_SkillsWithDI.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAI001;CA1812;IDE0051</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.DependencyInjection" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Program.cs

@@ -0,0 +1,210 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use Dependency Injection (DI) with Agent Skills.
+// It shows two approaches side-by-side, each handling a different conversion domain:
+//
+// 1. Code-defined skill (AgentInlineSkill) — converts distances (miles ↔ kilometers).
+//    Resources and scripts are inline delegates that resolve services from IServiceProvider.
+//
+// 2. Class-based skill (AgentClassSkill) — converts weights (pounds ↔ kilograms).
+//    Resources and scripts are encapsulated in a class, also resolving services from IServiceProvider.
+//
+// Both skills share the same ConversionService registered in the DI container,
+// showing that DI works identically regardless of how the skill is defined.
+// When prompted with a question spanning both domains, the agent uses both skills.
+
+using System.ComponentModel;
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- DI Container ---
+// Register application services that skill resources and scripts can resolve at execution time.
+ServiceCollection services = new();
+services.AddSingleton<ConversionService>();
+
+IServiceProvider serviceProvider = services.BuildServiceProvider();
+
+// =====================================================================
+// Approach 1: Code-Defined Skill with DI (AgentInlineSkill)
+// =====================================================================
+// Handles distance conversions (miles ↔ kilometers).
+// Resources and scripts are inline delegates. Each delegate can declare
+// an IServiceProvider parameter that the framework injects automatically.
+
+var distanceSkill = new AgentInlineSkill(
+    name: "distance-converter",
+    description: "Convert between distance units. Use when asked to convert miles to kilometers or kilometers to miles.",
+    instructions: """
+        Use this skill when the user asks to convert between distance units (miles and kilometers).
+
+        1. Review the distance-table resource to find the factor for the requested conversion.
+        2. Use the convert script, passing the value and factor from the table.
+        """)
+    .AddResource("distance-table", (IServiceProvider serviceProvider) =>
+    {
+        var service = serviceProvider.GetRequiredService<ConversionService>();
+        return service.GetDistanceTable();
+    })
+    .AddScript("convert", (double value, double factor, IServiceProvider serviceProvider) =>
+    {
+        var service = serviceProvider.GetRequiredService<ConversionService>();
+        return service.Convert(value, factor);
+    });
+
+// =====================================================================
+// Approach 2: Class-Based Skill with DI (AgentClassSkill)
+// =====================================================================
+// Handles weight conversions (pounds ↔ kilograms).
+// Resources and scripts are discovered via reflection using attributes.
+// Methods with an IServiceProvider parameter receive DI automatically.
+//
+// Alternatively, class-based skills can accept dependencies through their
+// constructor. Register the skill class itself in the ServiceCollection and
+// resolve it from the container:
+//
+//   services.AddSingleton<WeightConverterSkill>();
+//   var weightSkill = serviceProvider.GetRequiredService<WeightConverterSkill>();
+
+var weightSkill = new WeightConverterSkill();
+
+// --- Skills Provider ---
+// Both skills are registered with the same provider so the agent can use either one.
+var skillsProvider = new AgentSkillsProvider(distanceSkill, weightSkill);
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(
+        options: new ChatClientAgentOptions
+        {
+            Name = "UnitConverterAgent",
+            ChatOptions = new()
+            {
+                Instructions = "You are a helpful assistant that can convert units.",
+            },
+            AIContextProviders = [skillsProvider],
+        },
+        model: deploymentName,
+        services: serviceProvider);
+
+// --- Example: Unit conversion ---
+// This prompt spans both domains, so the agent will use both skills.
+Console.WriteLine("Converting units with DI-powered skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");
+
+// ---------------------------------------------------------------------------
+// Class-Based Skill
+// ---------------------------------------------------------------------------
+
+/// <summary>
+/// A weight-converter skill defined as a C# class that uses Dependency Injection.
+/// </summary>
+/// <remarks>
+/// This skill resolves <see cref="ConversionService"/> from the DI container
+/// in both its resource and script methods. Methods with an <see cref="IServiceProvider"/>
+/// parameter are automatically injected by the framework. Properties and methods annotated
+/// with <see cref="AgentSkillResourceAttribute"/> and <see cref="AgentSkillScriptAttribute"/>
+/// are automatically discovered via reflection.
+/// </remarks>
+internal sealed class WeightConverterSkill : AgentClassSkill<WeightConverterSkill>
+{
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; } = new(
+        "weight-converter",
+        "Convert between weight units. Use when asked to convert pounds to kilograms or kilograms to pounds.");
+
+    /// <inheritdoc/>
+    protected override string Instructions => """
+        Use this skill when the user asks to convert between weight units (pounds and kilograms).
+
+        1. Review the weight-table resource to find the factor for the requested conversion.
+        2. Use the convert script, passing the value and factor from the table.
+        3. Present the result clearly with both units.
+        """;
+
+    /// <summary>
+    /// Returns the weight conversion table from the DI-registered <see cref="ConversionService"/>.
+    /// </summary>
+    [AgentSkillResource("weight-table")]
+    [Description("Lookup table of multiplication factors for weight conversions.")]
+    private static string GetWeightTable(IServiceProvider serviceProvider)
+    {
+        var service = serviceProvider.GetRequiredService<ConversionService>();
+        return service.GetWeightTable();
+    }
+
+    /// <summary>
+    /// Converts a value by the given factor using the DI-registered <see cref="ConversionService"/>.
+    /// </summary>
+    [AgentSkillScript("convert")]
+    [Description("Multiplies a value by a conversion factor and returns the result as JSON.")]
+    private static string Convert(double value, double factor, IServiceProvider serviceProvider)
+    {
+        var service = serviceProvider.GetRequiredService<ConversionService>();
+        return service.Convert(value, factor);
+    }
+}
+
+// ---------------------------------------------------------------------------
+// Services
+// ---------------------------------------------------------------------------
+
+/// <summary>
+/// Provides conversion rates between units.
+/// In a real application this could call an external API, read from a database,
+/// or apply time-varying exchange rates.
+/// </summary>
+internal sealed class ConversionService
+{
+    /// <summary>
+    /// Returns a markdown table of supported distance conversions.
+    /// </summary>
+    public string GetDistanceTable() =>
+        """
+        # Distance Conversions
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | miles       | kilometers  | 1.60934  |
+        | kilometers  | miles       | 0.621371 |
+        """;
+
+    /// <summary>
+    /// Returns a markdown table of supported weight conversions.
+    /// </summary>
+    public string GetWeightTable() =>
+        """
+        # Weight Conversions
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | pounds      | kilograms   | 0.453592 |
+        | kilograms   | pounds      | 2.20462  |
+        """;
+
+    /// <summary>
+    /// Converts a value by the given factor and returns a JSON result.
+    /// </summary>
+    public string Convert(double value, double factor)
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    }
+}

dotnet/samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/README.md

@@ -0,0 +1,65 @@
+# Agent Skills with Dependency Injection
+
+This sample demonstrates how to use **Dependency Injection (DI)** with Agent Skills. It shows two approaches side-by-side, each handling a different conversion domain:
+
+1. **Code-defined skill** (`AgentInlineSkill`) — converts **distances** (miles ↔ kilometers)
+2. **Class-based skill** (`AgentClassSkill`) — converts **weights** (pounds ↔ kilograms)
+
+Both skills resolve the same `ConversionService` from the DI container. When prompted with a question spanning both domains, the agent uses both skills.
+
+## What It Shows
+
+- Registering application services in a `ServiceCollection`
+- Defining a **code-defined** skill (distance converter) with resources and scripts that resolve services from `IServiceProvider`
+- Defining a **class-based** skill (weight converter) with resources and scripts that resolve services from `IServiceProvider`
+- Passing the built `IServiceProvider` to the agent so skills can access DI services at execution time
+- Running a single prompt that exercises both skills to show they work together
+
+## How It Works
+
+1. A `ConversionService` is registered as a singleton in the DI container
+2. **Code-defined skill**: An `AgentInlineSkill` for distance conversions declares `IServiceProvider` as a parameter in its `AddResource` and `AddScript` delegates — the framework injects it automatically
+3. **Class-based skill**: A `WeightConverterSkill` class extends `AgentClassSkill` for weight conversions and uses `CreateResource`/`CreateScript` factory methods with `IServiceProvider` parameters
+4. Both skills resolve `ConversionService` from the provider — one for distance tables, the other for weight tables
+5. A single agent is created with both skills registered, and the service provider flows through to skill execution
+
+> **Tip:** Class-based skills can also accept dependencies through their **constructor**. Register the skill class in the `ServiceCollection` and resolve it from the container instead of calling `new` directly. This is useful when the skill itself needs injected services beyond what the resource/script delegates use.
+
+## How It Differs from Other Samples
+
+| Sample | Skill Type | DI Support |
+|--------|------------|------------|
+| [Step02](../Agent_Step02_CodeDefinedSkills/) | Code-defined (`AgentInlineSkill`) | No — static resources |
+| [Step03](../Agent_Step03_ClassBasedSkills/) | Class-based (`AgentClassSkill`) | No — static resources |
+| **Step05 (this)** | **Both code-defined and class-based** | **Yes — DI via `IServiceProvider`** |
+
+## Prerequisites
+
+- .NET 10
+- An Azure OpenAI deployment
+
+## Configuration
+
+Set the following environment variables:
+
+| Variable | Description |
+|---|---|
+| `AZURE_OPENAI_ENDPOINT` | Your Azure OpenAI endpoint URL |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Model deployment name (defaults to `gpt-5.4-mini`) |
+
+## Running the Sample
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with DI-powered skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```

dotnet/samples/02-agents/AgentSkills/README.md

@@ -1,7 +1,37 @@
 # AgentSkills Samples
 
-Samples demonstrating Agent Skills capabilities.
+Samples demonstrating Agent Skills capabilities. Each sample shows a different way to define and use skills.
 
 | Sample | Description |
 |--------|-------------|
-| [Agent_Step01_BasicSkills](Agent_Step01_BasicSkills/) | Using Agent Skills with a ChatClientAgent, including progressive disclosure and skill resources |
+| [Agent_Step01_FileBasedSkills](Agent_Step01_FileBasedSkills/) | Define skills as `SKILL.md` files on disk with reference documents. Uses a unit-converter skill. |
+| [Agent_Step02_CodeDefinedSkills](Agent_Step02_CodeDefinedSkills/) | Define skills entirely in C# code using `AgentInlineSkill`, with static/dynamic resources and scripts. |
+| [Agent_Step03_ClassBasedSkills](Agent_Step03_ClassBasedSkills/) | Define skills as C# classes using `AgentClassSkill`. |
+| [Agent_Step04_MixedSkills](Agent_Step04_MixedSkills/) | **(Advanced)** Combine file-based, code-defined, and class-based skills using `AgentSkillsProviderBuilder`. |
+| [Agent_Step05_SkillsWithDI](Agent_Step05_SkillsWithDI/) | Use Dependency Injection with both code-defined (`AgentInlineSkill`) and class-based (`AgentClassSkill`) skills. |
+
+## Key Concepts
+
+### Skill Types
+
+| Aspect | File-Based | Code-Defined | Class-Based |
+|--------|-----------|--------------|-------------|
+| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# | Classes extending `AgentClassSkill` |
+| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) | `CreateResource` factory methods |
+| Scripts | Supported via script runner delegate | `AddScript` delegates | `CreateScript` factory methods |
+| Discovery | Automatic from directory path | Explicit via constructor | Explicit via constructor |
+| Dynamic content | No (static files only) | Yes (factory delegates) | Yes (factory delegates) |
+| Sharing pattern | Copy skill directory | Inline or shared instances | Package in shared assemblies/NuGet |
+| DI support | No | Yes (via `IServiceProvider` parameter) | Yes (via `IServiceProvider` parameter) |
+
+### `AgentSkillsProvider` vs `AgentSkillsProviderBuilder`
+
+For single-source scenarios, use the `AgentSkillsProvider` constructors directly — they accept a skill directory path, a set of skills, or a custom source.
+
+Use `AgentSkillsProviderBuilder` for advanced scenarios where simple constructors are insufficient:
+
+- **Mixed skill types** — combine file-based, code-defined, and class-based skills in one provider
+- **Multiple file script runners** — use different script runners for different file skill directories
+- **Skill filtering** — include or exclude skills using a predicate
+
+See [Agent_Step04_MixedSkills](Agent_Step04_MixedSkills/) for a working example.

dotnet/samples/02-agents/AgentSkills/SubprocessScriptRunner.cs

@@ -0,0 +1,135 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Sample subprocess-based skill script runner.
+// Executes file-based skill scripts as local subprocesses.
+// This is provided for demonstration purposes only.
+
+using System.Diagnostics;
+using System.Text.Json;
+using Microsoft.Agents.AI;
+
+/// <summary>
+/// Executes file-based skill scripts as local subprocesses.
+/// </summary>
+/// <remarks>
+/// This runner uses the script's absolute path and converts the arguments
+/// to CLI arguments. When the LLM sends a JSON array, each element is used
+/// as a positional argument. It is intended for demonstration purposes only.
+/// </remarks>
+internal static class SubprocessScriptRunner
+{
+    /// <summary>
+    /// Runs a skill script as a local subprocess.
+    /// </summary>
+    public static async Task<object?> RunAsync(
+        AgentFileSkill skill,
+        AgentFileSkillScript script,
+        JsonElement? arguments,
+        IServiceProvider? serviceProvider,
+        CancellationToken cancellationToken)
+    {
+        if (!File.Exists(script.FullPath))
+        {
+            return $"Error: Script file not found: {script.FullPath}";
+        }
+
+        string extension = Path.GetExtension(script.FullPath);
+        string? interpreter = extension switch
+        {
+            ".py" => "python3",
+            ".js" => "node",
+            ".sh" => "bash",
+            ".ps1" => "pwsh",
+            _ => null,
+        };
+
+        var startInfo = new ProcessStartInfo
+        {
+            RedirectStandardOutput = true,
+            RedirectStandardError = true,
+            UseShellExecute = false,
+            CreateNoWindow = true,
+            WorkingDirectory = Path.GetDirectoryName(script.FullPath) ?? ".",
+        };
+
+        if (interpreter is not null)
+        {
+            startInfo.FileName = interpreter;
+            startInfo.ArgumentList.Add(script.FullPath);
+        }
+        else
+        {
+            startInfo.FileName = script.FullPath;
+        }
+
+        if (arguments is { ValueKind: JsonValueKind.Array } json)
+        {
+            // Positional CLI arguments
+            foreach (var element in json.EnumerateArray())
+            {
+                if (element.ValueKind != JsonValueKind.String)
+                {
+                    throw new InvalidOperationException(
+                        $"File-based skill scripts only accept string CLI arguments but received a JSON element of kind '{element.ValueKind}'. " +
+                        "All array elements must be JSON strings.");
+                }
+
+                startInfo.ArgumentList.Add(element.GetString()!);
+            }
+        }
+        else if (arguments is not null && arguments.Value.ValueKind != JsonValueKind.Null && arguments.Value.ValueKind != JsonValueKind.Undefined)
+        {
+            throw new InvalidOperationException(
+                $"Expected a JSON array of CLI arguments but received {arguments.Value.ValueKind}. " +
+                "File-based skill scripts expect positional arguments as a JSON array of strings.");
+        }
+
+        Process? process = null;
+        try
+        {
+            process = Process.Start(startInfo);
+            if (process is null)
+            {
+                return $"Error: Failed to start process for script '{script.Name}'.";
+            }
+
+            Task<string> outputTask = process.StandardOutput.ReadToEndAsync(cancellationToken);
+            Task<string> errorTask = process.StandardError.ReadToEndAsync(cancellationToken);
+
+            await process.WaitForExitAsync(cancellationToken).ConfigureAwait(false);
+
+            string output = await outputTask.ConfigureAwait(false);
+            string error = await errorTask.ConfigureAwait(false);
+
+            if (!string.IsNullOrEmpty(error))
+            {
+                output += $"\nStderr:\n{error}";
+            }
+
+            if (process.ExitCode != 0)
+            {
+                output += $"\nScript exited with code {process.ExitCode}";
+            }
+
+            return string.IsNullOrEmpty(output) ? "(no output)" : output.Trim();
+        }
+        catch (OperationCanceledException) when (cancellationToken.IsCancellationRequested)
+        {
+            // Kill the process on cancellation to avoid leaving orphaned subprocesses.
+            process?.Kill(entireProcessTree: true);
+            throw;
+        }
+        catch (OperationCanceledException)
+        {
+            throw;
+        }
+        catch (Exception ex)
+        {
+            return $"Error: Failed to execute script '{script.Name}': {ex.Message}";
+        }
+        finally
+        {
+            process?.Dispose();
+        }
+    }
+}

dotnet/samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step01_Running/Program.cs

@@ -5,20 +5,13 @@
 using Anthropic;
 using Anthropic.Core;
 using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
 
 var apiKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY") ?? throw new InvalidOperationException("ANTHROPIC_API_KEY is not set.");
 var model = Environment.GetEnvironmentVariable("ANTHROPIC_CHAT_MODEL_NAME") ?? "claude-haiku-4-5";
 
-AIAgent agent = new AnthropicClient(new ClientOptions { ApiKey = apiKey })
+AIAgent agent =
+    new AnthropicClient(new ClientOptions { ApiKey = apiKey })
     .AsAIAgent(model: model, instructions: "You are good at telling jokes.", name: "Joker");
 
 // Invoke the agent and output the text result.
-var response = await agent.RunAsync("Tell me a joke about a pirate.");
-Console.WriteLine(response);
-
-// Invoke the agent with streaming support.
-await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
-{
-    Console.WriteLine(update);
-}
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));

dotnet/samples/02-agents/AgentWithAnthropic/README.md

@@ -18,9 +18,9 @@ Before you begin, ensure you have the following prerequisites:
 
 **Note**: These samples use Anthropic Claude models. For more information, see [Anthropic documentation](https://docs.anthropic.com/).
 
-## Using Anthropic with Azure Foundry
+## Using Anthropic with Microsoft Foundry
 
-To use Anthropic with Azure Foundry, you can check the sample [AgentProviders/Agent_With_Anthropic](../AgentProviders/Agent_With_Anthropic/README.md) for more details.
+To use Anthropic with Microsoft Foundry, you can check the sample [AgentProviders/Agent_With_Anthropic](../AgentProviders/Agent_With_Anthropic/README.md) for more details.
 
 ## Samples
 

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step01_Interpreter/AgentWithCodeAct_Step01_Interpreter.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hyperlight\Microsoft.Agents.AI.Hyperlight.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step01_Interpreter/Program.cs

@@ -0,0 +1,30 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use HyperlightCodeActProvider as a sandboxed Python
+// code interpreter: the model can write and execute arbitrary Python code to
+// answer quantitative questions without calling any additional tools.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hyperlight;
+using OpenAI.Chat;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH") ?? throw new InvalidOperationException("HYPERLIGHT_PYTHON_GUEST_PATH is not set.");
+
+using var codeAct = new HyperlightCodeActProvider(HyperlightCodeActProviderOptions.CreateForWasm(guestPath));
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetChatClient(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions()
+    {
+        ChatOptions = new() { Instructions = "You are a helpful assistant. When the user asks something quantitative, write Python and call `execute_code` instead of guessing." },
+        AIContextProviders = [codeAct],
+    });
+
+Console.WriteLine(await agent.RunAsync("What is the 20th Fibonacci number?"));
+Console.WriteLine(await agent.RunAsync("Compute the mean and standard deviation of [1, 4, 9, 16, 25, 36]."));

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step01_Interpreter/README.md

@@ -0,0 +1,35 @@
+# AgentWithCodeAct_Step01_Interpreter
+
+A minimal CodeAct sample. The agent uses `HyperlightCodeActProvider` as a
+sandboxed Python interpreter: when the user asks something quantitative, the
+model writes Python and invokes the `execute_code` tool rather than answering
+from memory.
+
+## Configuration
+
+| Variable                       | Description                                                                               |
+|--------------------------------|-------------------------------------------------------------------------------------------|
+| `AZURE_OPENAI_ENDPOINT`        | Azure OpenAI endpoint. Required.                                                          |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI deployment. Defaults to `gpt-5.4-mini`.                                      |
+| `HYPERLIGHT_PYTHON_GUEST_PATH` | Absolute path to the Hyperlight Python guest module (`.wasm` or `.aot` file). Required.   |
+
+Authentication uses `DefaultAzureCredential`.
+
+## Getting the guest module
+
+The Python guest module is built from the
+[hyperlight-dev/hyperlight-sandbox](https://github.com/hyperlight-dev/hyperlight-sandbox)
+repository — see its README for the exact `cargo`/`just` invocations and
+the location of the resulting `.wasm` / `.aot` file. Set
+`HYPERLIGHT_PYTHON_GUEST_PATH` to the absolute path of that artifact
+before running the sample.
+
+Hyperlight requires a hardware virtualization back end on the host:
+KVM on Linux or WHP (Windows Hypervisor Platform) on Windows.
+
+## Run
+
+```shell
+cd AgentWithCodeAct_Step01_Interpreter
+dotnet run
+```

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step02_ToolEnabled/AgentWithCodeAct_Step02_ToolEnabled.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hyperlight\Microsoft.Agents.AI.Hyperlight.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step02_ToolEnabled/Program.cs

@@ -0,0 +1,52 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use HyperlightCodeActProvider with provider-owned
+// tools (exposed inside the sandbox via `call_tool(...)`). The model can
+// orchestrate those tools in a single Python block, reducing round-trips. A
+// sensitive tool (`send_email`) is additionally wrapped in
+// ApprovalRequiredAIFunction so any code that reaches it requires user approval
+// for the entire execute_code invocation.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hyperlight;
+using Microsoft.Extensions.AI;
+using OpenAI.Chat;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH") ?? throw new InvalidOperationException("HYPERLIGHT_PYTHON_GUEST_PATH is not set.");
+
+AIFunction fetchDocs = AIFunctionFactory.Create(
+    (string topic) => $"Docs for {topic}: (...)",
+    name: "fetch_docs",
+    description: "Fetch documentation for a given topic.");
+
+AIFunction queryData = AIFunctionFactory.Create(
+    (string query) => $"Rows for `{query}`: []",
+    name: "query_data",
+    description: "Run a read-only SQL-like query against the sample store.");
+
+AIFunction sendEmail = new ApprovalRequiredAIFunction(
+    AIFunctionFactory.Create(
+        (string to, string subject) => $"Sent '{subject}' to {to}.",
+        name: "send_email",
+        description: "Send an email on behalf of the user."));
+
+var options = HyperlightCodeActProviderOptions.CreateForWasm(guestPath);
+options.Tools = [fetchDocs, queryData, sendEmail];
+
+using var codeAct = new HyperlightCodeActProvider(options);
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetChatClient(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions()
+    {
+        ChatOptions = new() { Instructions = "You are a helpful assistant. Prefer orchestrating your work in a single `execute_code` block using `call_tool(...)` over issuing many direct tool calls." },
+        AIContextProviders = [codeAct],
+    });
+
+Console.WriteLine(await agent.RunAsync("Look up docs on 'retries' and query the 'orders' table, then summarize."));

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step02_ToolEnabled/README.md

@@ -0,0 +1,34 @@
+# AgentWithCodeAct_Step02_ToolEnabled
+
+Demonstrates adding provider-owned tools to `HyperlightCodeActProvider`. Those
+tools are **only** available to code running inside the sandbox via
+`call_tool("<name>", ...)` — they are never exposed to the model as direct
+tools. This lets the model orchestrate multiple tool calls in a single Python
+block.
+
+One tool (`send_email`) is wrapped in `ApprovalRequiredAIFunction`, which causes
+the entire `execute_code` invocation to require user approval when that tool
+is configured.
+
+## Configuration
+
+| Variable                       | Description                                                                               |
+|--------------------------------|-------------------------------------------------------------------------------------------|
+| `AZURE_OPENAI_ENDPOINT`        | Azure OpenAI endpoint. Required.                                                          |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI deployment. Defaults to `gpt-5.4-mini`.                                      |
+| `HYPERLIGHT_PYTHON_GUEST_PATH` | Absolute path to the Hyperlight Python guest module (`.wasm` or `.aot` file). Required.   |
+
+## Run
+
+```shell
+cd AgentWithCodeAct_Step02_ToolEnabled
+dotnet run
+```
+
+## Planned follow-up
+
+A more realistic "upload a file (e.g. an Excel workbook), have the agent
+analyze it with code" sample is planned as a separate step that will use
+`HostInputDirectory` together with a guest tool capable of reading the
+uploaded file. It will be added in a follow-up PR once the corresponding
+guest module support is in place.

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step03_ManualWiring/AgentWithCodeAct_Step03_ManualWiring.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Hyperlight\Microsoft.Agents.AI.Hyperlight.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step03_ManualWiring/Program.cs

@@ -0,0 +1,40 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to wire up CodeAct manually using
+// HyperlightExecuteCodeFunction rather than the AIContextProvider. Use this
+// when you want a fixed tool surface for the agent's lifetime and don't need
+// the per-run snapshot/registry semantics of HyperlightCodeActProvider.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hyperlight;
+using Microsoft.Extensions.AI;
+using OpenAI.Chat;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH") ?? throw new InvalidOperationException("HYPERLIGHT_PYTHON_GUEST_PATH is not set.");
+
+AIFunction calculate = AIFunctionFactory.Create(
+    (double a, double b) => a * b,
+    name: "multiply",
+    description: "Multiply two numbers.");
+
+var options = HyperlightCodeActProviderOptions.CreateForWasm(guestPath);
+options.Tools = [calculate];
+
+using var executeCode = new HyperlightExecuteCodeFunction(options);
+
+var instructions =
+    "You are a helpful assistant. When math is involved, solve it by writing Python "
+    + "and calling `execute_code` instead of computing values yourself.\n\n"
+    + executeCode.BuildInstructions(toolsVisibleToModel: false);
+
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetChatClient(deploymentName)
+    .AsAIAgent(instructions: instructions, tools: [executeCode]);
+
+Console.WriteLine(await agent.RunAsync("What is 12.3 * 4.5? Use the multiply tool from within `execute_code`."));

dotnet/samples/02-agents/AgentWithCodeAct/AgentWithCodeAct_Step03_ManualWiring/README.md

@@ -0,0 +1,21 @@
+# AgentWithCodeAct_Step03_ManualWiring
+
+Shows how to wire CodeAct manually using `HyperlightExecuteCodeFunction` as a
+direct agent tool instead of via an `AIContextProvider`. This is useful when
+the sandbox's tool surface and capabilities are fixed for the agent's
+lifetime, avoiding per-run snapshot/restore of the provider registry.
+
+## Configuration
+
+| Variable                       | Description                                                                               |
+|--------------------------------|-------------------------------------------------------------------------------------------|
+| `AZURE_OPENAI_ENDPOINT`        | Azure OpenAI endpoint. Required.                                                          |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI deployment. Defaults to `gpt-5.4-mini`.                                      |
+| `HYPERLIGHT_PYTHON_GUEST_PATH` | Absolute path to the Hyperlight Python guest module (`.wasm` or `.aot` file). Required.   |
+
+## Run
+
+```shell
+cd AgentWithCodeAct_Step03_ManualWiring
+dotnet run
+```

dotnet/samples/02-agents/AgentWithCodeAct/README.md

@@ -0,0 +1,16 @@
+# Agent Framework CodeAct (Hyperlight) Samples
+
+These samples show how to enable an agent to write and execute code in a
+Hyperlight-backed sandbox via the CodeAct pattern. Guest code can be pure
+Python (interpreter mode) or orchestrate host-provided tools through
+`call_tool(...)` — all inside a secure sandbox with opt-in filesystem and
+network access.
+
+|Sample|Description|
+|---|---|
+|[Code interpreter](./AgentWithCodeAct_Step01_Interpreter/)|Uses `HyperlightCodeActProvider` as a sandboxed Python interpreter with no host tools.|
+|[Tool-enabled CodeAct](./AgentWithCodeAct_Step02_ToolEnabled/)|Registers provider-owned tools that guest code can orchestrate via `call_tool(...)`, with an approval-required tool for sensitive actions.|
+|[Manual wiring](./AgentWithCodeAct_Step03_ManualWiring/)|Uses `HyperlightExecuteCodeFunction` directly as an agent tool when the sandbox configuration is fixed.|
+
+All samples require a Hyperlight Python guest module. Set
+`HYPERLIGHT_PYTHON_GUEST_PATH` to its absolute path before running.

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/Program.cs

@@ -12,7 +12,7 @@ using Microsoft.SemanticKernel.Connectors.InMemory;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
 
 // Create a vector store to store the chat messages in.

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/Program.cs

@@ -14,7 +14,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 var mem0ServiceUri = Environment.GetEnvironmentVariable("MEM0_ENDPOINT") ?? throw new InvalidOperationException("MEM0_ENDPOINT is not set.");
 var mem0ApiKey = Environment.GetEnvironmentVariable("MEM0_API_KEY") ?? throw new InvalidOperationException("MEM0_API_KEY is not set.");

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <OutputType>Exe</OutputType>
@@ -14,8 +14,7 @@
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.FoundryMemory\Microsoft.Agents.AI.FoundryMemory.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/Program.cs

@@ -1,24 +1,27 @@
 // Copyright (c) Microsoft. All rights reserved.
 
 // This sample shows how to use the FoundryMemoryProvider to persist and recall memories for an agent.
-// The sample stores conversation messages in an Azure AI Foundry memory store and retrieves relevant
+// The sample stores conversation messages in a Microsoft Foundry memory store and retrieves relevant
 // memories for subsequent invocations, even across new sessions.
 //
-// Note: Memory extraction in Azure AI Foundry is asynchronous and takes time. This sample demonstrates
+// Note: Memory extraction in Microsoft Foundry is asynchronous and takes time. This sample demonstrates
 // a simple polling approach to wait for memory updates to complete before querying.
 
 using System.Text.Json;
 using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.FoundryMemory;
+using Microsoft.Agents.AI.Foundry;
 
 string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? "memory-store-sample";
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";
 
 // Create an AIProjectClient for Foundry with Azure Identity authentication.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 DefaultAzureCredential credential = new();
 AIProjectClient projectClient = new(new Uri(foundryEndpoint), credential);
 
@@ -33,11 +36,15 @@ FoundryMemoryProvider memoryProvider = new(
     memoryStoreName,
     stateInitializer: _ => new(new FoundryMemoryProviderScope("sample-user-123")));
 
-AIAgent agent = await projectClient.CreateAIAgentAsync(deploymentName,
-    options: new ChatClientAgentOptions()
+ChatClientAgent agent = projectClient.AsAIAgent(
+    new ChatClientAgentOptions()
     {
         Name = "TravelAssistantWithFoundryMemory",
-        ChatOptions = new() { Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details." },
+        ChatOptions = new()
+        {
+            ModelId = deploymentName,
+            Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details."
+        },
         AIContextProviders = [memoryProvider]
     });
 
@@ -54,7 +61,7 @@ await memoryProvider.EnsureStoredMemoriesDeletedAsync(session);
 Console.WriteLine(await agent.RunAsync("Hi there! My name is Taylor and I'm planning a hiking trip to Patagonia in November.", session));
 Console.WriteLine(await agent.RunAsync("I'm travelling with my sister and we love finding scenic viewpoints.", session));
 
-// Memory extraction in Azure AI Foundry is asynchronous and takes time to process.
+// Memory extraction in Microsoft Foundry is asynchronous and takes time to process.
 // WhenUpdatesCompletedAsync polls all pending updates and waits for them to complete.
 Console.WriteLine("\nWaiting for Foundry Memory to process updates...");
 await memoryProvider.WhenUpdatesCompletedAsync();

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/README.md

@@ -1,6 +1,6 @@
-# Agent with Memory Using Azure AI Foundry
+# Agent with Memory Using Microsoft Foundry
 
-This sample demonstrates how to create and run an agent that uses Azure AI Foundry's managed memory service to extract and retrieve individual memories across sessions.
+This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories across sessions.
 
 ## Features Demonstrated
 
@@ -13,20 +13,20 @@ This sample demonstrates how to create and run an agent that uses Azure AI Found
 
 ## Prerequisites
 
-1. Azure subscription with Azure AI Foundry project
-2. Azure OpenAI resource with a chat model deployment (e.g., gpt-4o-mini) and an embedding model deployment (e.g., text-embedding-ada-002)
+1. Azure subscription with Microsoft Foundry project
+2. Azure OpenAI resource with a chat model deployment (e.g., gpt-5.4-mini) and an embedding model deployment (e.g., text-embedding-ada-002)
 3. .NET 10.0 SDK
 4. Azure CLI logged in (`az login`)
 
 ## Environment Variables
 
 ```bash
-# Azure AI Foundry project endpoint and memory store name
+# Microsoft Foundry project endpoint and memory store name
 export AZURE_AI_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
 export AZURE_AI_MEMORY_STORE_ID="my_memory_store"
 
 # Model deployment names (models deployed in your Foundry project)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
 export AZURE_AI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-ada-002"
 ```
 
@@ -48,10 +48,10 @@ The agent will:
 
 ## Key Differences from Mem0
 
-| Aspect | Mem0 | Azure AI Foundry Memory |
+| Aspect | Mem0 | Microsoft Foundry Memory |
 |--------|------|------------------------|
 | Authentication | API Key | Azure Identity (DefaultAzureCredential) |
 | Scope | ApplicationId, UserId, AgentId, ThreadId | Single `Scope` string |
 | Memory Types | Single memory store | User Profile + Chat Summary |
-| Hosting | Mem0 cloud or self-hosted | Azure AI Foundry managed service |
+| Hosting | Mem0 cloud or self-hosted | Microsoft Foundry managed service |
 | Store Creation | N/A (automatic) | Explicit via `EnsureMemoryStoreCreatedAsync` |

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/Program.cs

@@ -15,7 +15,7 @@ using OpenAI.Chat;
 using SampleApp;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/README.md

@@ -13,7 +13,7 @@ This sample demonstrates how to create a custom `ChatHistoryProvider` that keeps
 
 - [.NET 10 SDK](https://dotnet.microsoft.com/download/dotnet/10.0)
 - An Azure OpenAI resource with:
-  - A chat deployment (e.g., `gpt-4o-mini`)
+  - A chat deployment (e.g., `gpt-5.4-mini`)
   - An embedding deployment (e.g., `text-embedding-3-large`)
 
 ## Configuration
@@ -23,7 +23,7 @@ Set the following environment variables:
 | Variable | Description | Default |
 |---|---|---|
 | `AZURE_OPENAI_ENDPOINT` | Your Azure OpenAI endpoint URL | *(required)* |
-| `AZURE_OPENAI_DEPLOYMENT_NAME` | Chat model deployment name | `gpt-4o-mini` |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Chat model deployment name | `gpt-5.4-mini` |
 | `AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME` | Embedding model deployment name | `text-embedding-3-large` |
 
 ## Running the Sample

dotnet/samples/02-agents/AgentWithMemory/README.md

@@ -1,4 +1,4 @@
-# Agent Framework Retrieval Augmented Generation (RAG)
+# Agent Framework Retrieval Augmented Generation (RAG)
 
 These samples show how to create an agent with the Agent Framework that uses Memory to remember previous conversations or facts from previous conversations.
 
@@ -7,7 +7,7 @@ These samples show how to create an agent with the Agent Framework that uses Mem
 |[Chat History memory](./AgentWithMemory_Step01_ChatHistoryMemory/)|This sample demonstrates how to enable an agent to remember messages from previous conversations.|
 |[Memory with MemoryStore](./AgentWithMemory_Step02_MemoryUsingMem0/)|This sample demonstrates how to create and run an agent that uses the Mem0 service to extract and retrieve individual memories.|
 |[Custom Memory Implementation](../../01-get-started/04_memory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
-|[Memory with Azure AI Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Azure AI Foundry's managed memory service to extract and retrieve individual memories.|
+|[Memory with Microsoft Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories.|
 |[Bounded Chat History with Overflow](./AgentWithMemory_Step05_BoundedChatHistory/)|This sample demonstrates how to create a bounded chat history provider that overflows older messages to a vector store and recalls them as memories.|
 
-> **See also**: [Memory Search with Foundry Agents](../FoundryAgents/FoundryAgents_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Azure Foundry Agents.
+> **See also**: [Memory Search with Foundry Agents](../AgentsWithFoundry/Agent_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Microsoft Foundry agents.

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Program.cs

@@ -4,28 +4,14 @@
 
 using System.ClientModel;
 using Microsoft.Agents.AI;
-using OpenAI;
-using OpenAI.Chat;
+using OpenAI.Responses;
 
 var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
-AIAgent agent = new OpenAIClient(apiKey)
-    .GetChatClient(model)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
+AIAgent agent =
+    new ResponsesClient(new ApiKeyCredential(apiKey))
+    .AsAIAgent(model: model, instructions: "You are good at telling jokes.", name: "Joker");
 
-UserChatMessage chatMessage = new("Tell me a joke about a pirate.");
-
-// Invoke the agent and output the text result.
-ChatCompletion chatCompletion = await agent.RunAsync([chatMessage]);
-Console.WriteLine(chatCompletion.Content.Last().Text);
-
-// Invoke the agent with streaming support.
-AsyncCollectionResult<StreamingChatCompletionUpdate> completionUpdates = agent.RunStreamingAsync([chatMessage]);
-await foreach (StreamingChatCompletionUpdate completionUpdate in completionUpdates)
-{
-    if (completionUpdate.ContentUpdate.Count > 0)
-    {
-        Console.WriteLine(completionUpdate.ContentUpdate[0].Text);
-    }
-}
+// Once you have the agent, you can invoke it like any other AIAgent.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Program.cs

@@ -7,7 +7,7 @@ using Microsoft.Extensions.AI;
 using OpenAI;
 
 var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5";
+var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 var client = new OpenAIClient(apiKey)
         .GetResponsesClient()

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step03_CreateFromChatClient/Program.cs

@@ -7,7 +7,7 @@ using OpenAI.Chat;
 using OpenAIChatClientSample;
 
 string apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-string model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+string model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 // Create a ChatClient directly from OpenAIClient
 ChatClient chatClient = new OpenAIClient(apiKey).GetChatClient(model);

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step03_CreateFromChatClient/README.md

@@ -13,7 +13,7 @@ This sample demonstrates how to create an AI agent directly from an `OpenAI.Chat
 1. Set the required environment variables:
    ```bash
    set OPENAI_API_KEY=your_api_key_here
-   set OPENAI_CHAT_MODEL_NAME=gpt-4o-mini
+   set OPENAI_CHAT_MODEL_NAME=gpt-5.4-mini
    ```
 
 2. Run the sample:

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/Program.cs

@@ -7,7 +7,7 @@ using OpenAI.Responses;
 using OpenAIResponseClientSample;
 
 var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 // Create a ResponsesClient directly from OpenAIClient
 ResponsesClient responseClient = new OpenAIClient(apiKey).GetResponsesClient();

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/README.md

@@ -13,7 +13,7 @@ This sample demonstrates how to create an AI agent directly from an `OpenAI.Resp
 1. Set the required environment variables:
    ```bash
    set OPENAI_API_KEY=your_api_key_here
-   set OPENAI_CHAT_MODEL_NAME=gpt-4o-mini
+   set OPENAI_CHAT_MODEL_NAME=gpt-5.4-mini
    ```
 
 2. Run the sample:

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step05_Conversation/Program.cs

@@ -15,7 +15,7 @@ using OpenAI.Chat;
 using OpenAI.Conversations;
 
 string apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-string model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+string model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 // Create a ConversationClient directly from OpenAIClient
 OpenAIClient openAIClient = new(apiKey);
@@ -73,16 +73,28 @@ foreach (ClientResult result in getConversationItemsResults.GetRawPages())
     using JsonDocument getConversationItemsResultAsJson = JsonDocument.Parse(result.GetRawResponse().Content.ToString());
     foreach (JsonElement element in getConversationItemsResultAsJson.RootElement.GetProperty("data").EnumerateArray())
     {
+        // Skip non-message items (e.g. tool calls, reasoning) that lack a "role" property
+        if (!element.TryGetProperty("role"u8, out var roleElement))
+        {
+            continue;
+        }
+
         string messageId = element.GetProperty("id"u8).ToString();
-        string messageRole = element.GetProperty("role"u8).ToString();
+        string messageRole = roleElement.ToString();
         Console.WriteLine($"    Message ID: {messageId}");
         Console.WriteLine($"    Message Role: {messageRole}");
 
-        foreach (var content in element.GetProperty("content").EnumerateArray())
+        if (element.TryGetProperty("content"u8, out var contentElement))
         {
-            string messageContentText = content.GetProperty("text"u8).ToString();
-            Console.WriteLine($"    Message Text: {messageContentText}");
+            foreach (var content in contentElement.EnumerateArray())
+            {
+                if (content.TryGetProperty("text"u8, out var textElement))
+                {
+                    Console.WriteLine($"    Message Text: {textElement}");
+                }
+            }
         }
+
         Console.WriteLine();
     }
 }