Merge branch 'main' into dependabot/nuget/dotnet/AWSSDK.Extensions.Bedrock.MEAI-4.0.6.4

2026-06-16 21:04:09 +08:00 · 2026-03-30 14:06:41 +01:00
parent b9e61371f1 f45fc7d402
commit 387ec346f6
193 changed files with 4319 additions and 3899 deletions
@@ -0,0 +1,125 @@
+---
+status: accepted
+contact: rogerbarreto
+date: 2026-03-06
+deciders: rogerbarreto, alliscode
+consulted: ""
+informed: ""
+---
+
+# Foundry agent surface stays centered on `ChatClientAgent`
+
+## Context
+
+The Microsoft Foundry integration exposes two distinct usage patterns:
+
+1. Direct Responses usage, where callers provide model, instructions, and tools at runtime.
+2. Server-side versioned agents, where callers create and manage `AgentVersion` resources through `AIProjectClient.Agents`.
+
+We briefly explored adding public wrapper types such as `FoundryAgent`, `FoundryVersionedAgent`, and `FoundryResponsesChatClient` to make those paths feel more specialized. That direction created extra public types, duplicated existing `ChatClientAgent` behavior, and pushed samples toward compatibility helpers instead of the native Azure SDK flow.
+
+## Decision
+
+Keep the public surface centered on `ChatClientAgent`.
+
+- Direct Responses scenarios use `AIProjectClient.AsAIAgent(...)`.
+- Server-side versioned scenarios use native `AIProjectClient.Agents` APIs to create or retrieve agent resources, then wrap `AgentRecord` or `AgentVersion` with `AIProjectClient.AsAIAgent(...)`.
+- Compatibility helpers such as `AIProjectClient.CreateAIAgentAsync(...)` and `AIProjectClient.GetAIAgentAsync(...)` remain only as obsolete migration shims.
+- Public wrapper types `FoundryAgent`, `FoundryVersionedAgent`, `FoundryResponsesChatClient`, and `FoundryResponsesChatClientAgent` are not part of the chosen direction.
+
+## Why
+
+- `ChatClientAgent` is already the framework abstraction used everywhere else.
+- `AIProjectClient` is the native Azure SDK entry point for versioned agent lifecycle operations.
+- A single agent abstraction avoids parallel type hierarchies for the same backend.
+- Samples become clearer when they show either:
+  - direct Responses construction via `AIProjectClient.AsAIAgent(...)`, or
+  - native Foundry resource management via `AIProjectClient.Agents`.
+
+## Consequences
+
+### Direct Responses path
+
+Use the convenience overloads on `AIProjectClient`:
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+ChatClientAgent agent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+Or use composed `ChatClientAgent`
+
+```csharp
+ProjectResponsesClient projectResponsesClient = new(new Uri(endpoint), new DefaultAzureCredential(), new AgentReference($"model:{deploymentName}"));
+
+ChatClientAgent agent = new(
+    chatClient: projectResponsesClient.AsIChatClient(),
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+This path is code-first and does not create a persistent server-side agent.
+
+### Versioned agent path
+
+Use the convenience overloads on `AIProjectClient`:
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+AgentVersion version = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "JokerAgent",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(deploymentName)
+        {
+            Instructions = "You are good at telling jokes."
+        }));
+
+ChatClientAgent agent = aiProjectClient.AsAIAgent(version);
+```
+
+Or use composed `ChatClientAgent`
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+AgentVersion version = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "JokerAgent",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(deploymentName)
+        {
+            Instructions = "You are good at telling jokes."
+        }));
+
+ProjectResponsesClient projectResponsesClient = aiProjectClient
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClientForAgent(new AgentReference(version.Name, version.Version));
+
+ChatClientAgent agent = new(
+    chatClient: projectResponsesClient.AsIChatClient(),
+    name: "JokerAgent");
+```
+
+### Samples
+
+- `FoundryAgents/` samples show the direct Responses path with `AIProjectClient.AsAIAgent(...)`.
+- `FoundryVersionedAgents/` samples should show native `AIProjectClient.Agents` create/get/delete flows plus `AsAIAgent(...)`.
+
+### Compatibility APIs
+
+Obsolete helper extensions remain only to ease migration of existing code. New samples and new guidance should not be written against them.
+
+## Rejected direction
+
+Do not introduce or preserve separate public wrapper types whose main purpose is to forward to `ChatClientAgent` while carrying Foundry-specific naming.
+
+That approach:
+
+- duplicates lifecycle concepts already present on `AIProjectClient`,
+- fragments the public API,
+- complicates samples and docs,
+- and makes migration harder by encouraging wrapper-specific affordances.
@@ -1,4 +1,4 @@
-<Solution>
+<Solution>
  <Configurations>
    <BuildType Name="Debug" />
    <BuildType Name="Publish" />
@@ -121,6 +121,34 @@
    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step03_UsingFunctionTools/Agent_Anthropic_Step03_UsingFunctionTools.csproj" />
    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step04_UsingSkills/Agent_Anthropic_Step04_UsingSkills.csproj" />
  </Folder>
+  <Folder Name="/Samples/02-agents/AgentsWithFoundry/">
+    <File Path="samples/02-agents/AgentsWithFoundry/README.md" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step00_FoundryAgentLifecycle/Agent_Step00_FoundryAgentLifecycle.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step01_Basics/Agent_Step01_Basics.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step02.1_MultiturnConversation/Agent_Step02.1_MultiturnConversation.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step02.2_MultiturnWithServerConversations/Agent_Step02.2_MultiturnWithServerConversations.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step03_UsingFunctionTools/Agent_Step03_UsingFunctionTools.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step07_Observability/Agent_Step07_Observability.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step08_DependencyInjection/Agent_Step08_DependencyInjection.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step09_UsingMcpClientAsTools/Agent_Step09_UsingMcpClientAsTools.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step10_UsingImages/Agent_Step10_UsingImages.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step11_AsFunctionTool/Agent_Step11_AsFunctionTool.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step12_Middleware/Agent_Step12_Middleware.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step13_Plugins/Agent_Step13_Plugins.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step14_CodeInterpreter/Agent_Step14_CodeInterpreter.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step15_ComputerUse/Agent_Step15_ComputerUse.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step16_FileSearch/Agent_Step16_FileSearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step17_OpenAPITools/Agent_Step17_OpenAPITools.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step18_BingCustomSearch/Agent_Step18_BingCustomSearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step19_SharePoint/Agent_Step19_SharePoint.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step20_MicrosoftFabric/Agent_Step20_MicrosoftFabric.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step21_WebSearch/Agent_Step21_WebSearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step22_MemorySearch/Agent_Step22_MemorySearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step23_LocalMCP/Agent_Step23_LocalMCP.csproj" />
+  </Folder>
  <Folder Name="/Samples/02-agents/AgentWithMemory/">
    <File Path="samples/02-agents/AgentWithMemory/README.md" />
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj" />
@@ -143,35 +171,6 @@
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
  </Folder>
-  <Folder Name="/Samples/02-agents/FoundryAgents/">
-    <File Path="samples/02-agents/FoundryAgents/README.md" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/FoundryAgents_Evaluations_Step01_RedTeaming.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection/FoundryAgents_Evaluations_Step02_SelfReflection.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step01.1_Basics/FoundryAgents_Step01.1_Basics.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step01.2_Running/FoundryAgents_Step01.2_Running.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step02_MultiturnConversation/FoundryAgents_Step02_MultiturnConversation.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/FoundryAgents_Step03_UsingFunctionTools.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/FoundryAgents_Step04_UsingFunctionToolsWithApprovals.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step05_StructuredOutput/FoundryAgents_Step05_StructuredOutput.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step06_PersistedConversations/FoundryAgents_Step06_PersistedConversations.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step07_Observability/FoundryAgents_Step07_Observability.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step08_DependencyInjection/FoundryAgents_Step08_DependencyInjection.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step09_UsingMcpClientAsTools/FoundryAgents_Step09_UsingMcpClientAsTools.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step11_AsFunctionTool/FoundryAgents_Step11_AsFunctionTool.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step12_Middleware/FoundryAgents_Step12_Middleware.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step13_Plugins/FoundryAgents_Step13_Plugins.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/FoundryAgents_Step14_CodeInterpreter.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step15_ComputerUse/FoundryAgents_Step15_ComputerUse.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step16_FileSearch/FoundryAgents_Step16_FileSearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step17_OpenAPITools/FoundryAgents_Step17_OpenAPITools.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step18_BingCustomSearch/FoundryAgents_Step18_BingCustomSearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step19_SharePoint/FoundryAgents_Step19_SharePoint.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step20_MicrosoftFabric/FoundryAgents_Step20_MicrosoftFabric.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step21_WebSearch/FoundryAgents_Step21_WebSearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step22_MemorySearch/FoundryAgents_Step22_MemorySearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step23_LocalMCP/FoundryAgents_Step23_LocalMCP.csproj" />
-  </Folder>
  <Folder Name="/Samples/02-agents/ModelContextProtocol/">
    <File Path="samples/02-agents/ModelContextProtocol/README.md" />
    <Project Path="samples/02-agents/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
@@ -318,8 +317,8 @@
  <Folder Name="/Samples/05-end-to-end/AspNetAgentAuthorization/">
    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/docker-compose.yml" />
    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/README.md" />
-    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj" />
    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj" />
+    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj" />
  </Folder>
  <Folder Name="/Solution Items/">
    <File Path=".editorconfig" />
@@ -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;
@@ -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
        }
@@ -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);
                }
            }
        }
@@ -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;
                    }
@@ -6,6 +6,7 @@ using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;

 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";
@@ -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.");
+AgentVersion newJokerAgentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    JokerName,
+    new AgentVersionCreationOptions(new PromptAgentDefinition(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>()!;
+AgentRecord jokerAgentRecord = await aiProjectClient.Agents.GetAgentAsync(JokerName);
+FoundryAgent jokerAgentLatest = aiProjectClient.AsAIAgent(jokerAgentRecord);
+AgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();

 // The AIAgent version can be accessed via the GetService method.
 Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
@@ -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."));
@@ -11,6 +11,7 @@ using System.Text.Json;
 using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
 using Microsoft.Agents.AI.FoundryMemory;

 string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
@@ -19,6 +20,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLO
 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 +37,15 @@ FoundryMemoryProvider memoryProvider = new(
    memoryStoreName,
    stateInitializer: _ => new(new FoundryMemoryProviderScope("sample-user-123")));

-AIAgent agent = await projectClient.CreateAIAgentAsync(deploymentName,
-    options: new ChatClientAgentOptions()
+FoundryAgent 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]
    });

@@ -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.

@@ -10,4 +10,4 @@ These samples show how to create an agent with the Agent Framework that uses Mem
 |[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.|
 |[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 Azure Foundry agents.
@@ -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";

-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."));
@@ -4,11 +4,13 @@

 using System.ClientModel;
 using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
+using Microsoft.Agents.AI.AzureAI;
 using OpenAI;
 using OpenAI.Files;
+using OpenAI.Responses;
 using OpenAI.VectorStores;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
@@ -37,14 +39,20 @@ ClientResult<VectorStore> vectorStoreCreate = await vectorStoreClient.CreateVect
    FileIds = { uploadResult.Value.Id }
 });

-var fileSearchTool = new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreCreate.Value.Id)] };
+// Use the native OpenAI SDK FileSearchTool directly with the vector store ID.
+#pragma warning disable OPENAI001
+FileSearchTool fileSearchTool = new([vectorStoreCreate.Value.Id]);
+#pragma warning restore OPENAI001

-AIAgent agent = await aiProjectClient
-    .CreateAIAgentAsync(
-        model: deploymentName,
-        name: "AskContoso",
-        instructions: "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
-        tools: [fileSearchTool]);
+AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "AskContoso",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
+            Tools = { fileSearchTool }
+        }));
+FoundryAgent agent = aiProjectClient.AsAIAgent(agentVersion);

 AgentSession session = await agent.CreateSessionAsync();

@@ -3,6 +3,7 @@
 // This sample shows how to expose an AI agent as an MCP tool.

 using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.DependencyInjection;
@@ -18,11 +19,17 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYME
 var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());

 // Create a server side agent and expose it as an AIAgent.
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    instructions: "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
-    name: "Joker",
-    description: "An agent that tells jokes.");
+AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "Joker",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
+        })
+    {
+        Description = "An agent that tells jokes.",
+    });
+AIAgent agent = aiProjectClient.AsAIAgent(agentVersion);

 // Convert the agent to an AIFunction and then to an MCP tool.
 // The agent name and description will be used as the mcp tool name and description.
@@ -189,9 +189,9 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent
        // Regex patterns for PII detection (simplified for demonstration)
        Regex[] piiPatterns =
        [
-            new(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled), // Phone number (e.g., 123-456-7890)
-            new(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled), // Email address
-            new(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled) // Full name (e.g., John Doe)
+            MyRegex(), // Phone number (e.g., 123-456-7890)
+            EmailRegex(), // Email address
+            FullNameRegex() // Full name (e.g., John Doe)
        ];

        foreach (var pattern in piiPatterns)
@@ -309,3 +309,15 @@ internal sealed class DateTimeContextProvider : MessageAIContextProvider
            ]);
    }
 }
+
+internal partial class Program
+{
+    [GeneratedRegex(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled)]
+    private static partial Regex MyRegex();
+
+    [GeneratedRegex(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled)]
+    private static partial Regex EmailRegex();
+
+    [GeneratedRegex(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled)]
+    private static partial Regex FullNameRegex();
+}
@@ -17,10 +17,10 @@ var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_AI_BING_CONNECT
 PersistentAgentsAdministrationClientOptions persistentAgentsClientOptions = new();
 persistentAgentsClientOptions.Retry.NetworkTimeout = TimeSpan.FromMinutes(20);

-// Get a client to create/retrieve server side agents with.
 // 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.
+// Get a client to create/retrieve server side agents with.
 PersistentAgentsClient persistentAgentsClient = new(endpoint, new DefaultAzureCredential(), persistentAgentsClientOptions);

 // Define and configure the Deep Research tool.
@@ -23,12 +23,14 @@ Before running this sample, ensure you have:

 Pay special attention to the purple `Note` boxes in the Azure documentation.

-**Note**: The Bing Connection ID must be from the **project**, not the resource. It has the following format:
+**Note**: The Bing Grounding Connection ID must be the **full ARM resource URI** from the project, not just the connection name. It has the following format:

 ```
-/subscriptions/<sub_id>/resourceGroups/<rg_name>/providers/<provider_name>/accounts/<account_name>/projects/<project_name>/connections/<connection_name>
+/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>
 ```

+You can find this in the Azure AI Foundry portal under **Management > Connected resources**, or retrieve it programmatically via the connections API (`.id` property).
+
 ## Environment Variables

 Set the following environment variables:
@@ -37,8 +39,8 @@ Set the following environment variables:
 # Replace with your Azure AI Foundry project endpoint
 $env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"

-# Replace with your Bing connection ID from the project
-$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/.../connections/your-bing-connection"
+# Replace with your Bing Grounding connection ID (full ARM resource URI)
+$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>"

 # Optional, defaults to o3-deep-research
 $env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"
@@ -24,12 +24,12 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT
 Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>
 {
    // In a real implementation, this method would connect to a calendar service
-    return new string[]
-    {
+    return
+    [
        "Doctor's appointment today at 15:00",
        "Team meeting today at 17:00",
        "Birthday party today at 20:00"
-    };
+    ];
 };

 // Create an agent with an AI context provider attached that aggregates two other providers:
@@ -87,7 +87,7 @@ namespace SampleApp
    internal sealed class TodoListAIContextProvider : AIContextProvider
    {
        private static List<string> GetTodoItems(AgentSession? session)
-            => session?.StateBag.GetValue<List<string>>(nameof(TodoListAIContextProvider)) ?? new List<string>();
+            => session?.StateBag.GetValue<List<string>>(nameof(TodoListAIContextProvider)) ?? [];

        private static void SetTodoItems(AgentSession? session, List<string> items)
            => session?.StateBag.SetValue(nameof(TodoListAIContextProvider), items);
@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <OutputType>Exe</OutputType>
@@ -0,0 +1,36 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create, use, and clean up a FoundryAgent backed by a server-side
+// versioned agent in Azure AI Foundry. It demonstrates the full lifecycle:
+// create agent version -> wrap as FoundryAgent -> run -> delete.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI.AzureAI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string JokerName = "JokerAgent";
+
+// Create the AIProjectClient to manage server-side agents.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Create a server-side agent version using the native SDK.
+AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    JokerName,
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are good at telling jokes.",
+        }));
+
+// Wrap the agent version as a FoundryAgent using the AsAIAgent extension.
+FoundryAgent agent = aiProjectClient.AsAIAgent(agentVersion);
+
+// 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."));
+
+// Cleanup: deletes the agent and all its versions.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,23 @@
+# Agent Step 00 - FoundryAgent Lifecycle
+
+This sample demonstrates the full lifecycle of a `FoundryAgent` backed by a server-side versioned agent in Microsoft Foundry: create → run → delete.
+
+## Prerequisites
+
+- A Microsoft Foundry project endpoint
+- A model deployment name (defaults to `gpt-4o-mini`)
+- Azure CLI installed and authenticated
+
+## Environment Variables
+
+| Variable | Description | Required |
+| --- | --- | --- |
+| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | Yes |
+| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Model deployment name | No (defaults to `gpt-4o-mini`) |
+
+## Running the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step00_FoundryAgentLifecycle
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,20 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and run a basic agent with AIProjectClient.AsAIAgent(...).
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent =
+    new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
+
+// 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."));
@@ -0,0 +1,55 @@
+# Creating and Running a Basic Agent with the Responses API
+
+This sample demonstrates how to create and run a basic AI agent using the `ChatClientAgent`, which uses the Microsoft Foundry Responses API directly without creating server-side agent definitions.
+
+## What this sample demonstrates
+
+- Creating a `ChatClientAgent` with instructions and a model
+- Running a simple single-turn conversation
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- 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 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
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step01_Basics
+```
+
+## Alternative: Composable approach
+
+You can also create the same agent by composing the underlying `IChatClient` directly. This gives you full control over the chat client pipeline:
+
+```csharp
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = new ChatClientAgent(
+    chatClient: aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClient().AsIChatClient(deploymentName),
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+This approach is useful when you need to customize the chat client pipeline or swap providers (e.g., Anthropic, OpenAI) while keeping the same agent code.
@@ -9,8 +9,7 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
-    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
  </ItemGroup>

 </Project>
@@ -0,0 +1,26 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create a multi-turn conversation agent using sessions.
+// Context is preserved across multiple runs via response ID chaining in the session.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
+
+// Create a session to maintain context across multiple runs.
+AgentSession session = await agent.CreateSessionAsync();
+
+// First turn
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+
+// Second turn — the agent remembers the first turn via the session.
+Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
@@ -0,0 +1,36 @@
+# Multi-turn Conversation
+
+This sample demonstrates how to implement multi-turn conversations where context is preserved across multiple agent runs using sessions and response ID chaining.
+
+## What this sample demonstrates
+
+- Creating an agent with instructions
+- Using sessions to maintain conversation context across multiple runs
+- Response ID chaining for multi-turn conversations
+- No server-side conversation creation required
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- 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 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
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step02.1_MultiturnConversation
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,34 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use server-side conversations with a FoundryAgent.
+// Server-side conversations persist on the Foundry service and are visible in the Foundry Project UI.
+// Use this when you need conversation history to be stored and accessible server-side.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+FoundryAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
+
+// CreateConversationSessionAsync creates a server-side ProjectConversation
+// that persists on the Foundry service and is visible in the Foundry Project UI.
+AgentSession session = await agent.CreateConversationSessionAsync();
+
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
+
+// Streaming with server-side conversation context.
+await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("Tell me another joke, but about a ninja this time.", session))
+{
+    Console.Write(update);
+}
+
+Console.WriteLine();
@@ -0,0 +1,36 @@
+# Multi-turn Conversation with Server-Side Conversations
+
+This sample demonstrates how to use server-side conversations with a `FoundryAgent`. Server-side conversations persist on the Foundry service and are visible in the Foundry Project UI, making them ideal when you need conversation history to be stored and accessible server-side.
+
+## What this sample demonstrates
+
+- Creating a `FoundryAgent` with instructions
+- Using `CreateConversationSessionAsync` to create a server-side `ProjectConversation`
+- Multi-turn conversations with both text and streaming output
+- Server-side conversation persistence visible in the Foundry Project UI
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- 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 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
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step02.2_MultiturnWithServerConversations
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,41 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use function tools.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+// Define the function tool.
+AITool tool = AIFunctionFactory.Create(GetWeather);
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a AIAgent with function tools.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant that can get weather information.",
+    name: "WeatherAssistant",
+    tools: [tool]);
+
+// Non-streaming agent interaction with function tools.
+AgentSession session = await agent.CreateSessionAsync();
+Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", session));
+
+// Streaming agent interaction with function tools.
+session = await agent.CreateSessionAsync();
+await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("What is the weather like in Amsterdam?", session))
+{
+    Console.Write(update);
+}
@@ -0,0 +1,37 @@
+# Using Function Tools with the Responses API
+
+This sample demonstrates how to use function tools with the `ChatClientAgent`, allowing the agent to call custom functions to retrieve information.
+
+## What this sample demonstrates
+
+- Creating function tools using `AIFunctionFactory`
+- Passing function tools to a `ChatClientAgent`
+- Running agents with function tools (text output)
+- Running agents with function tools (streaming output)
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- 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 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"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step03_UsingFunctionTools
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,9 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

 // This sample demonstrates how to use an agent with function tools that require a human in the loop for approvals.
-// It shows both non-streaming and streaming agent interactions using weather-related tools.
-// If the agent is hosted in a service, with a remote user, combine this sample with the Persisted Conversations sample to persist the chat history
-// while the agent is waiting for user input.

 using System.ComponentModel;
 using Azure.AI.Projects;
@@ -11,18 +8,13 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// Create a sample function tool that the agent can use.
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

-const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
-const string AssistantName = "WeatherAssistant";
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-// Get a client to create/retrieve/delete server side agents with Azure 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.
@@ -30,16 +22,16 @@ AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredent

 ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)));

-// Create AIAgent directly
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [approvalTool]);
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant that can get weather information.",
+    name: "WeatherAssistant",
+    tools: [approvalTool]);

 // Call the agent with approval-required function tools.
-// The agent will request approval before invoking the function.
 AgentSession session = await agent.CreateSessionAsync();
 AgentResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", session);

 // Check if there are any approval requests.
-// For simplicity, we are assuming here that only function approvals are pending.
 List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();

 while (approvalRequests.Count > 0)
@@ -53,13 +45,8 @@ while (approvalRequests.Count > 0)
            return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
        });

-    // Pass the user input responses back to the agent for further processing.
    response = await agent.RunAsync(userInputMessages, session);
-
    approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
 }

 Console.WriteLine($"\nAgent: {response}");
-
-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,30 @@
+# Using Function Tools with Approvals via the Responses API
+
+This sample demonstrates how to use function tools that require human-in-the-loop approval before execution.
+
+## What this sample demonstrates
+
+- Creating function tools that require approval using `ApprovalRequiredAIFunction`
+- Handling approval requests from the agent
+- Passing approval responses back to the agent
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step04_UsingFunctionToolsWithApprovals
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -15,29 +15,23 @@ using SampleApp;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-const string AssistantInstructions = "You are a helpful assistant that extracts structured information about people.";
-const string AssistantName = "StructuredOutputAssistant";
-
-// Get a client to create/retrieve/delete server side agents with Azure 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.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Create ChatClientAgent directly
-ChatClientAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    new ChatClientAgentOptions()
+AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
+{
+    Name = "StructuredOutputAssistant",
+    ChatOptions = new()
    {
-        Name = AssistantName,
-        ChatOptions = new()
-        {
-            Instructions = AssistantInstructions,
-            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
-        }
-    });
+        ModelId = deploymentName,
+        Instructions = "You are a helpful assistant that extracts structured information about people.",
+        ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
+    }
+});

-// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke the agent with some unstructured input.
+// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output.
 AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");

 // Access the structured output via the Result property of the agent response.
@@ -46,39 +40,21 @@ Console.WriteLine($"Name: {response.Result.Name}");
 Console.WriteLine($"Age: {response.Result.Age}");
 Console.WriteLine($"Occupation: {response.Result.Occupation}");

-// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
-ChatClientAgent agentWithPersonInfo = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    new ChatClientAgentOptions()
-    {
-        Name = AssistantName,
-        ChatOptions = new()
-        {
-            Instructions = AssistantInstructions,
-            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
-        }
-    });
+// Invoke the agent with streaming support, then deserialize the assembled response.
+IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Please provide information about Jane Doe, who is a 28-year-old data scientist.");

-// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
-IAsyncEnumerable<AgentResponseUpdate> updates = agentWithPersonInfo.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
-
-// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
-// then deserialize the response into the PersonInfo class.
 PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>((await updates.ToAgentResponseAsync()).Text, JsonSerializerOptions.Web)
    ?? throw new InvalidOperationException("Failed to deserialize the streamed response into PersonInfo.");

-Console.WriteLine("Assistant Output:");
+Console.WriteLine("\nStreaming Assistant Output:");
 Console.WriteLine($"Name: {personInfo.Name}");
 Console.WriteLine($"Age: {personInfo.Age}");
 Console.WriteLine($"Occupation: {personInfo.Occupation}");

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-
 namespace SampleApp
 {
    /// <summary>
-    /// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
+    /// Represents information about a person.
    /// </summary>
    [Description("Information about a person including their name, age, and occupation")]
    public class PersonInfo
@@ -0,0 +1,29 @@
+# Structured Output with the Responses API
+
+This sample demonstrates how to configure an agent to produce structured output using JSON schema.
+
+## What this sample demonstrates
+
+- Using `RunAsync<T>()` to get typed structured output from the agent
+- Deserializing streamed responses into structured types
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step05_StructuredOutput
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use a simple AI agent with a conversation that can be persisted to disk.
+// This sample shows how to persist and resume conversations.

 using System.Text.Json;
 using Azure.AI.Projects;
@@ -10,16 +10,14 @@ using Microsoft.Agents.AI;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-const string JokerInstructions = "You are good at telling jokes.";
-const string JokerName = "JokerAgent";
-
-// Get a client to create/retrieve/delete server side agents with Azure 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.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: JokerInstructions);
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");

 // Start a new session for the agent conversation.
 AgentSession session = await agent.CreateSessionAsync();
@@ -42,6 +40,3 @@ AgentSession resumedSession = await agent.DeserializeSessionAsync(reloadedSerial

 // Run the agent again with the resumed session.
 Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedSession));
-
-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,30 @@
+# Persisted Conversations with the Responses API
+
+This sample demonstrates how to persist and resume agent conversations using session serialization.
+
+## What this sample demonstrates
+
+- Serializing agent sessions to JSON for persistence
+- Saving and loading sessions from disk
+- Resuming conversations with preserved context
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step06_PersistedConversations
+```
@@ -9,8 +9,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
-    <PackageReference Include="Azure.Identity" />
    <PackageReference Include="Azure.Monitor.OpenTelemetry.Exporter" />
    <PackageReference Include="OpenTelemetry" />
    <PackageReference Include="OpenTelemetry.Exporter.Console" />
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend that logs telemetry using OpenTelemetry.
+// This sample shows how to add OpenTelemetry observability to an agent.

 using Azure.AI.Projects;
 using Azure.Identity;
@@ -9,15 +9,11 @@ using Microsoft.Agents.AI;
 using OpenTelemetry;
 using OpenTelemetry.Trace;

+string? applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-string? applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");

-const string JokerInstructions = "You are good at telling jokes.";
-const string JokerName = "JokerAgent";
-
-// Create TracerProvider with console exporter
-// This will output the telemetry data to the console.
+// Create TracerProvider with console exporter.
 string sourceName = Guid.NewGuid().ToString("N");
 TracerProviderBuilder tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
    .AddSource(sourceName)
@@ -28,14 +24,16 @@ if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
 }
 using var tracerProvider = tracerProviderBuilder.Build();

-// Get a client to create/retrieve/delete server side agents with Azure 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.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Define the agent you want to create. (Prompt Agent in this case)
-AIAgent agent = (await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: JokerInstructions))
+AIAgent agent = aiProjectClient
+    .AsAIAgent(
+        deploymentName,
+        instructions: "You are good at telling jokes.",
+        name: "JokerAgent")
    .AsBuilder()
    .UseOpenTelemetry(sourceName: sourceName)
    .Build();
@@ -48,8 +46,7 @@ Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session
 session = await agent.CreateSessionAsync();
 await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("Tell me a joke about a pirate.", session))
 {
-    Console.WriteLine(update);
+    Console.Write(update);
 }

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+Console.WriteLine();
@@ -0,0 +1,31 @@
+# Observability with the Responses API
+
+This sample demonstrates how to add OpenTelemetry observability to an agent using console and Azure Monitor exporters.
+
+## What this sample demonstrates
+
+- Configuring OpenTelemetry tracing with console exporter
+- Optional Azure Application Insights integration
+- Using `.AsBuilder().UseOpenTelemetry()` to add telemetry to the agent
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:APPLICATIONINSIGHTS_CONNECTION_STRING="..."  # Optional
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step07_Observability
+```
@@ -11,8 +11,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
-    <PackageReference Include="Azure.Identity" />
    <PackageReference Include="Microsoft.Extensions.Hosting" />
  </ItemGroup>

@@ -0,0 +1,83 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use dependency injection to register a AIAgent and use it from a hosted service.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using SampleApp;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+
+// Create a host builder that we will register services with and then run.
+HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
+
+// Add the AI agent to the service collection.
+builder.Services.AddSingleton(agent);
+
+// Add a sample service that will use the agent to respond to user input.
+builder.Services.AddHostedService<SampleService>();
+
+// Build and run the host.
+using IHost host = builder.Build();
+await host.RunAsync().ConfigureAwait(false);
+
+namespace SampleApp
+{
+    /// <summary>
+    /// A sample service that uses an AI agent to respond to user input.
+    /// </summary>
+    internal sealed class SampleService(AIAgent agent, IHostApplicationLifetime appLifetime) : IHostedService
+    {
+        private AgentSession? _session;
+
+        public async Task StartAsync(CancellationToken cancellationToken)
+        {
+            this._session = await agent.CreateSessionAsync(cancellationToken);
+            _ = this.RunAsync(appLifetime.ApplicationStopping);
+        }
+
+        public async Task RunAsync(CancellationToken cancellationToken)
+        {
+            await Task.Delay(100, cancellationToken);
+
+            while (!cancellationToken.IsCancellationRequested)
+            {
+                Console.WriteLine("\nAgent: Ask me to tell you a joke about a specific topic. To exit just press Ctrl+C or enter without any input.\n");
+                Console.Write("> ");
+                string? input = Console.ReadLine();
+
+                if (string.IsNullOrWhiteSpace(input))
+                {
+                    appLifetime.StopApplication();
+                    break;
+                }
+
+                await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, this._session, cancellationToken: cancellationToken))
+                {
+                    Console.Write(update);
+                }
+
+                Console.WriteLine();
+            }
+        }
+
+        public Task StopAsync(CancellationToken cancellationToken)
+        {
+            Console.WriteLine("\nShutting down...");
+            return Task.CompletedTask;
+        }
+    }
+}
@@ -0,0 +1,30 @@
+# Dependency Injection with the Responses API
+
+This sample demonstrates how to register a `ChatClientAgent` in a dependency injection container and use it from a hosted service.
+
+## What this sample demonstrates
+
+- Registering `ChatClientAgent` as an `AIAgent` in the service collection
+- Using the agent from a `IHostedService` with an interactive chat loop
+- Streaming responses in a hosted service context
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step08_DependencyInjection
+```
@@ -6,16 +6,15 @@

    <Nullable>enable</Nullable>
    <ImplicitUsings>enable</ImplicitUsings>
-    <NoWarn>$(NoWarn);CA1812</NoWarn>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.Projects" />
    <PackageReference Include="ModelContextProtocol" />
  </ItemGroup>

  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
  </ItemGroup>

@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use MCP client tools with an agent.
+// It connects to the Microsoft Learn MCP server via HTTP and uses its tools.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using ModelContextProtocol.Client;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// Connect to the Microsoft Learn MCP server via HTTP (Streamable HTTP transport).
+Console.WriteLine("Connecting to MCP server at https://learn.microsoft.com/api/mcp ...");
+
+await using McpClient mcpClient = await McpClient.CreateAsync(new HttpClientTransport(new()
+{
+    Endpoint = new Uri("https://learn.microsoft.com/api/mcp"),
+    Name = "Microsoft Learn MCP",
+}));
+
+// Retrieve the list of tools available on the MCP server.
+IList<McpClientTool> mcpTools = await mcpClient.ListToolsAsync();
+Console.WriteLine($"MCP tools available: {string.Join(", ", mcpTools.Select(t => t.Name))}");
+
+List<AITool> agentTools = [.. mcpTools.Cast<AITool>()];
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant that can help with Microsoft documentation questions. Use the Microsoft Learn MCP tool to search for documentation.",
+    name: "DocsAgent",
+    tools: agentTools);
+
+Console.WriteLine($"Agent '{agent.Name}' created. Asking a question...\n");
+
+const string Prompt = "How does one create an Azure storage account using az cli?";
+Console.WriteLine($"User: {Prompt}\n");
+Console.WriteLine($"Agent: {await agent.RunAsync(Prompt)}");
@@ -0,0 +1,29 @@
+# Using MCP Client as Tools with the Responses API
+
+This sample shows how to use MCP (Model Context Protocol) client tools with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Connecting to an MCP server via HTTP client transport
+- Retrieving MCP tools and passing them to a `ChatClientAgent`
+- Using MCP tools for agent interactions without server-side agent creation
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+- Node.js installed (for npx/MCP server)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="assets\walkway.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use Image Multi-Modality with an AI agent.
+// This sample shows how to use image multi-modality with an agent.

 using Azure.AI.Projects;
 using Azure.Identity;
@@ -10,29 +10,25 @@ using Microsoft.Extensions.AI;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";

-const string VisionInstructions = "You are a helpful agent that can analyze images";
-const string VisionName = "VisionAgent";
-
-// Get a client to create/retrieve/delete server side agents with Azure 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.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Define the agent you want to create. (Prompt Agent in this case)
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: VisionName, model: deploymentName, instructions: VisionInstructions);
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful agent that can analyze images.",
+    name: "VisionAgent");

 ChatMessage message = new(ChatRole.User, [
    new TextContent("What do you see in this image?"),
-    await DataContent.LoadFromAsync("Assets/walkway.jpg"),
+    await DataContent.LoadFromAsync("assets/walkway.jpg"),
 ]);

 AgentSession session = await agent.CreateSessionAsync();

 await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(message, session))
 {
-    Console.WriteLine(update);
+    Console.Write(update);
 }

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+Console.WriteLine();
@@ -0,0 +1,30 @@
+# Using Images with the Responses API
+
+This sample demonstrates how to use image multi-modality with an agent.
+
+## What this sample demonstrates
+
+- Loading images using `DataContent.LoadFromAsync`
+- Sending images alongside text to the agent
+- Streaming the agent's image analysis response
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and a vision-capable model deployment (e.g., `gpt-4o`)
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step10_UsingImages
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use an Azure Foundry Agents AI agent as a function tool.
+// This sample shows how to use one agent as a function tool for another agent.

 using System.ComponentModel;
 using Azure.AI.Projects;
@@ -8,43 +8,29 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-const string WeatherInstructions = "You answer questions about the weather.";
-const string WeatherName = "WeatherAgent";
-const string MainInstructions = "You are a helpful assistant who responds in French.";
-const string MainName = "MainAgent";
-
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Create the weather agent with function tools.
 AITool weatherTool = AIFunctionFactory.Create(GetWeather);
-AIAgent weatherAgent = await aiProjectClient.CreateAIAgentAsync(
-    name: WeatherName,
-    model: deploymentName,
-    instructions: WeatherInstructions,
+AIAgent weatherAgent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You answer questions about the weather.",
+    name: "WeatherAgent",
    tools: [weatherTool]);

-// Create the main agent, and provide the weather agent as a function tool.
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    name: MainName,
-    model: deploymentName,
-    instructions: MainInstructions,
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant who responds in French.",
+    name: "MainAgent",
    tools: [weatherAgent.AsAIFunction()]);

 // Invoke the agent and output the text result.
 AgentSession session = await agent.CreateSessionAsync();
 Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", session));
-
-// Cleanup by agent name removes the agent versions created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-await aiProjectClient.Agents.DeleteAgentAsync(weatherAgent.Name);
@@ -0,0 +1,30 @@
+# Agent as a Function Tool with the Responses API
+
+This sample demonstrates how to use one agent as a function tool for another agent.
+
+## What this sample demonstrates
+
+- Creating a specialized agent (weather) with function tools
+- Exposing an agent as a function tool using `.AsAIFunction()`
+- Composing agents where one agent delegates to another
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step11_AsFunctionTool
+```
@@ -3,15 +3,13 @@
  <PropertyGroup>
    <OutputType>Exe</OutputType>
    <TargetFrameworks>net10.0</TargetFrameworks>
-    
+
    <Nullable>enable</Nullable>
    <ImplicitUsings>enable</ImplicitUsings>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.Projects" />
  </ItemGroup>

  <ItemGroup>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows multiple middleware layers working together with Azure Foundry Agents:
+// This sample shows multiple middleware layers working together with a ChatClientAgent:
 // agent run (PII filtering and guardrails),
 // function invocation (logging and result overrides), and human-in-the-loop
 // approval workflows for sensitive function calls.
@@ -12,19 +12,6 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-// Get Azure AI Foundry configuration from environment variables
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = System.Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
-
-const string AssistantInstructions = "You are an AI assistant that helps people find information.";
-const string AssistantName = "InformationAssistant";
-
-// Get a client to create/retrieve/delete server side agents with Azure 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.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";
@@ -33,14 +20,20 @@ static string GetWeather([Description("The location to get the weather for.")] s
 static string GetDateTime()
    => DateTimeOffset.Now.ToString();

+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
 AITool dateTimeTool = AIFunctionFactory.Create(GetDateTime, name: nameof(GetDateTime));
 AITool getWeatherTool = AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather));

-// Define the agent you want to create. (Prompt Agent in this case)
-AIAgent originalAgent = await aiProjectClient.CreateAIAgentAsync(
-    name: AssistantName,
-    model: deploymentName,
-    instructions: AssistantInstructions,
+AIAgent originalAgent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are an AI assistant that helps people find information.",
+    name: "InformationAssistant",
    tools: [getWeatherTool, dateTimeTool]);

 // Adding middleware to the agent level
@@ -63,24 +56,17 @@ AgentResponse piiResponse = await middlewareEnabledAgent.RunAsync("My name is Jo
 Console.WriteLine($"Pii filtered response: {piiResponse}");

 Console.WriteLine("\n\n=== Example 3: Agent function middleware ===");
-
-// Agent function middleware support is limited to agents that wraps a upstream ChatClientAgent or derived from it.
-
 AgentResponse functionCallResponse = await middlewareEnabledAgent.RunAsync("What's the current time and the weather in Seattle?", session);
 Console.WriteLine($"Function calling response: {functionCallResponse}");

 // Special per-request middleware agent.
 Console.WriteLine("\n\n=== Example 4: Middleware with human in the loop function approval ===");

-AIAgent humanInTheLoopAgent = await aiProjectClient.CreateAIAgentAsync(
+AIAgent humanInTheLoopAgent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a Human in the loop testing AI assistant that helps people find information.",
    name: "HumanInTheLoopAgent",
-    model: deploymentName,
-    instructions: "You are an Human in the loop testing AI assistant that helps people find information.",
-
-    // Adding a function with approval required
    tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)))]);

-// Using the ConsolePromptingApprovalMiddleware for a specific request to handle user approval during function calls.
 AgentResponse response = await humanInTheLoopAgent
    .AsBuilder()
    .Use(ConsolePromptingApprovalMiddleware, null)
@@ -108,7 +94,6 @@ async ValueTask<object?> FunctionCallOverrideWeather(AIAgent agent, FunctionInvo

    if (context.Function.Name == nameof(GetWeather))
    {
-        // Override the result of the GetWeather function
        result = "The weather is sunny with a high of 25°C.";
    }
    Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 2 Post-Invoke");
@@ -118,18 +103,16 @@ async ValueTask<object?> FunctionCallOverrideWeather(AIAgent agent, FunctionInvo
 // This middleware redacts PII information from input and output messages.
 async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
 {
-    // Redact PII information from input messages
    var filteredMessages = FilterMessages(messages);
    Console.WriteLine("Pii Middleware - Filtered Messages Pre-Run");

-    var response = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken).ConfigureAwait(false);
+    var agentResponse = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken).ConfigureAwait(false);

-    // Redact PII information from output messages
-    response.Messages = FilterMessages(response.Messages);
+    agentResponse.Messages = FilterMessages(agentResponse.Messages);

    Console.WriteLine("Pii Middleware - Filtered Messages Post-Run");

-    return response;
+    return agentResponse;

    static IList<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
    {
@@ -138,11 +121,10 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent

    static string FilterPii(string content)
    {
-        // Regex patterns for PII detection (simplified for demonstration)
        Regex[] piiPatterns = [
-            new(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled), // Phone number (e.g., 123-456-7890)
-                    new(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled), // Email address
-                    new(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled) // Full name (e.g., John Doe)
+            MyRegex(),
+            EmailRegex(),
+            FullNameRegex()
        ];

        foreach (var pattern in piiPatterns)
@@ -157,20 +139,17 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent
 // This middleware enforces guardrails by redacting certain keywords from input and output messages.
 async Task<AgentResponse> GuardrailMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
 {
-    // Redact keywords from input messages
    var filteredMessages = FilterMessages(messages);

    Console.WriteLine("Guardrail Middleware - Filtered messages Pre-Run");

-    // Proceed with the agent run
-    var response = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken);
+    var agentResponse = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken);

-    // Redact keywords from output messages
-    response.Messages = FilterMessages(response.Messages);
+    agentResponse.Messages = FilterMessages(agentResponse.Messages);

    Console.WriteLine("Guardrail Middleware - Filtered messages Post-Run");

-    return response;
+    return agentResponse;

    List<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
    {
@@ -194,16 +173,13 @@ async Task<AgentResponse> GuardrailMiddleware(IEnumerable<ChatMessage> messages,
 // This middleware handles Human in the loop console interaction for any user approval required during function calling.
 async Task<AgentResponse> ConsolePromptingApprovalMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
 {
-    AgentResponse response = await innerAgent.RunAsync(messages, session, options, cancellationToken);
+    AgentResponse agentResponse = await innerAgent.RunAsync(messages, session, options, cancellationToken);

-    // For simplicity, we are assuming here that only function approvals are pending.
-    List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+    List<ToolApprovalRequestContent> approvalRequests = agentResponse.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();

    while (approvalRequests.Count > 0)
    {
-        // Ask the user to approve each function call request.
-        // Pass the user input responses back to the agent for further processing.
-        response.Messages = approvalRequests
+        agentResponse.Messages = approvalRequests
            .ConvertAll(functionApprovalRequest =>
            {
                Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {((FunctionCallContent)functionApprovalRequest.ToolCall).Name}");
@@ -211,13 +187,22 @@ async Task<AgentResponse> ConsolePromptingApprovalMiddleware(IEnumerable<ChatMes
                return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
            });

-        response = await innerAgent.RunAsync(response.Messages, session, options, cancellationToken);
+        agentResponse = await innerAgent.RunAsync(agentResponse.Messages, session, options, cancellationToken);

-        approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+        approvalRequests = agentResponse.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
    }

-    return response;
+    return agentResponse;
 }

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(middlewareEnabledAgent.Name);
+internal partial class Program
+{
+    [GeneratedRegex(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled)]
+    private static partial Regex MyRegex();
+
+    [GeneratedRegex(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled)]
+    private static partial Regex EmailRegex();
+
+    [GeneratedRegex(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled)]
+    private static partial Regex FullNameRegex();
+}
@@ -0,0 +1,31 @@
+# Middleware with the Responses API
+
+This sample demonstrates multiple middleware layers working together: PII filtering, guardrails, function invocation logging, and human-in-the-loop approval.
+
+## What this sample demonstrates
+
+- Agent-level run middleware (PII filtering, guardrail enforcement)
+- Function-level middleware (logging, result overrides)
+- Human-in-the-loop approval workflows for sensitive function calls
+- Using `.AsBuilder().Use()` to compose middleware
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step12_Middleware
+```
@@ -10,13 +10,12 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
  </ItemGroup>

  <ItemGroup>
    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
  </ItemGroup>
-  
+
 </Project>
@@ -0,0 +1,153 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use plugins with an AI agent. Plugin classes can
+// depend on other services that need to be injected. In this sample, the
+// AgentPlugin class uses the WeatherProvider and CurrentTimeProvider classes
+// to get weather and current time information. Both services are registered
+// in the service collection and injected into the plugin.
+// Plugin classes may have many methods, but only some are intended to be used
+// as AI functions. The AsAITools method of the plugin class shows how to specify
+// which methods should be exposed to the AI agent.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using SampleApp;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string AssistantInstructions = "You are a helpful assistant that helps people find information.";
+const string AssistantName = "PluginAssistant";
+
+// Create a service collection to hold the agent plugin and its dependencies.
+ServiceCollection services = new();
+services.AddSingleton<WeatherProvider>();
+services.AddSingleton<CurrentTimeProvider>();
+services.AddSingleton<AgentPlugin>(); // The plugin depends on WeatherProvider and CurrentTimeProvider registered above.
+
+IServiceProvider serviceProvider = services.BuildServiceProvider();
+
+// 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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a ChatClientAgent with the options-based constructor to pass services.
+AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
+{
+    Name = AssistantName,
+    ChatOptions = new() { ModelId = deploymentName, Instructions = AssistantInstructions, Tools = serviceProvider.GetRequiredService<AgentPlugin>().AsAITools().ToList() }
+},
+    services: serviceProvider);
+
+// Invoke the agent and output the text result.
+AgentSession session = await agent.CreateSessionAsync();
+Console.WriteLine(await agent.RunAsync("Tell me current time and weather in Seattle.", session));
+
+namespace SampleApp
+{
+    /// <summary>
+    /// The agent plugin that provides weather and current time information.
+    /// </summary>
+    internal sealed class AgentPlugin
+    {
+        private readonly WeatherProvider _weatherProvider;
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="AgentPlugin"/> class.
+        /// </summary>
+        /// <param name="weatherProvider">The weather provider to get weather information.</param>
+        public AgentPlugin(WeatherProvider weatherProvider)
+        {
+            this._weatherProvider = weatherProvider;
+        }
+
+        /// <summary>
+        /// Gets the weather information for the specified location.
+        /// </summary>
+        /// <remarks>
+        /// This method demonstrates how to use the dependency that was injected into the plugin class.
+        /// </remarks>
+        /// <param name="location">The location to get the weather for.</param>
+        /// <returns>The weather information for the specified location.</returns>
+        public string GetWeather(string location)
+        {
+            return this._weatherProvider.GetWeather(location);
+        }
+
+        /// <summary>
+        /// Gets the current date and time for the specified location.
+        /// </summary>
+        /// <remarks>
+        /// This method demonstrates how to resolve a dependency using the service provider passed to the method.
+        /// </remarks>
+        /// <param name="sp">The service provider to resolve the <see cref="CurrentTimeProvider"/>.</param>
+        /// <param name="location">The location to get the current time for.</param>
+        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
+        public DateTimeOffset GetCurrentTime(IServiceProvider sp, string location)
+        {
+            CurrentTimeProvider currentTimeProvider = sp.GetRequiredService<CurrentTimeProvider>();
+            return currentTimeProvider.GetCurrentTime(location);
+        }
+
+        /// <summary>
+        /// Returns the functions provided by this plugin.
+        /// </summary>
+        /// <remarks>
+        /// In real world scenarios, a class may have many methods and only a subset of them may be intended to be exposed as AI functions.
+        /// This method demonstrates how to explicitly specify which methods should be exposed to the AI agent.
+        /// </remarks>
+        /// <returns>The functions provided by this plugin.</returns>
+        public IEnumerable<AITool> AsAITools()
+        {
+            yield return AIFunctionFactory.Create(this.GetWeather);
+            yield return AIFunctionFactory.Create(this.GetCurrentTime);
+        }
+    }
+
+    internal sealed class WeatherProvider
+    {
+        private readonly string _weatherSummary = "cloudy with a high of 15°C";
+
+        /// <summary>
+        /// The weather provider that returns weather information.
+        /// </summary>
+        /// <summary>
+        /// Gets the weather information for the specified location.
+        /// </summary>
+        /// <remarks>
+        /// The weather information is hardcoded for demonstration purposes.
+        /// In a real application, this could call a weather API to get actual weather data.
+        /// </remarks>
+        /// <param name="location">The location to get the weather for.</param>
+        /// <returns>The weather information for the specified location.</returns>
+        public string GetWeather(string location)
+        {
+            return $"The weather in {location} is {this._weatherSummary}.";
+        }
+    }
+
+    internal sealed class CurrentTimeProvider
+    {
+        private readonly TimeProvider _timeProvider = TimeProvider.System;
+
+        /// <summary>
+        /// Provides the current date and time.
+        /// </summary>
+        /// <remarks>
+        /// This class returns the current date and time using the system's clock.
+        /// </remarks>
+        /// <summary>
+        /// Gets the current date and time.
+        /// </summary>
+        /// <param name="location">The location to get the current time for (not used in this implementation).</param>
+        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
+        public DateTimeOffset GetCurrentTime(string location)
+        {
+            return this._timeProvider.GetLocalNow();
+        }
+    }
+}
@@ -0,0 +1,29 @@
+# Using Plugins with the Responses API
+
+This sample shows how to use plugins with a `ChatClientAgent` using the Responses API directly, with dependency injection for plugin services.
+
+## What this sample demonstrates
+
+- Creating plugin classes with injected dependencies
+- Registering services and building a service provider
+- Passing `services` to the `ChatClientAgent` via the options-based constructor
+- Using `AIFunctionFactory` to expose plugin methods as AI tools
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -9,7 +9,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
    <PackageReference Include="Azure.Identity" />
  </ItemGroup>

@@ -1,61 +1,31 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use Code Interpreter Tool with AI Agents.
+// This sample shows how to use Code Interpreter Tool with AIProjectClient.AsAIAgent(...).

 using System.Text;
 using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using OpenAI.Assistants;
-using OpenAI.Responses;
+
+const string AgentInstructions = "You are a personal math tutor. When asked a math question, write and run code using the python tool to answer the question.";
+const string AgentName = "CoderAgent-RAPI";

 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-const string AgentInstructions = "You are a personal math tutor. When asked a math question, write and run code using the python tool to answer the question.";
-const string AgentNameMEAI = "CoderAgent-MEAI";
-const string AgentNameNative = "CoderAgent-NATIVE";
-
-// Get a client to create/retrieve/delete server side agents with Azure 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.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-// Option 1 - Using HostedCodeInterpreterTool + AgentOptions (MEAI + AgentFramework)
-// Create the server side agent version
-AIAgent agentOption1 = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    name: AgentNameMEAI,
+AIAgent agent = aiProjectClient.AsAIAgent(
+    deploymentName,
    instructions: AgentInstructions,
+    name: AgentName,
    tools: [new HostedCodeInterpreterTool() { Inputs = [] }]);

-// Option 2 - Using PromptAgentDefinition SDK native type
-// Create the server side agent version
-AIAgent agentOption2 = await aiProjectClient.CreateAIAgentAsync(
-    name: AgentNameNative,
-    creationOptions: new AgentVersionCreationOptions(
-        new PromptAgentDefinition(model: deploymentName)
-        {
-            Instructions = AgentInstructions,
-            Tools = {
-                ResponseTool.CreateCodeInterpreterTool(
-                    new CodeInterpreterToolContainer(
-                        CodeInterpreterToolContainerConfiguration.CreateAutomaticContainerConfiguration(fileIds: [])
-                    )
-                ),
-            }
-        })
-);
-
-// Either invoke option1 or option2 agent, should have same result
-// Option 1
-AgentResponse response = await agentOption1.RunAsync("I need to solve the equation sin(x) + x^2 = 42");
-
-// Option 2
-// AgentResponse response = await agentOption2.RunAsync("I need to solve the equation sin(x) + x^2 = 42");
+AgentResponse response = await agent.RunAsync("I need to solve the equation sin(x) + x^2 = 42");

 // Get the CodeInterpreterToolCallContent
 CodeInterpreterToolCallContent? toolCallContent = response.Messages.SelectMany(m => m.Contents).OfType<CodeInterpreterToolCallContent>().FirstOrDefault();
@@ -87,7 +57,3 @@ foreach (AIAnnotation annotation in response.Messages.SelectMany(m => m.Contents
            """);
    }
 }
-
-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agentOption1.Name);
-await aiProjectClient.Agents.DeleteAgentAsync(agentOption2.Name);
@@ -0,0 +1,28 @@
+# Code Interpreter with the Responses API
+
+This sample shows how to use the Code Interpreter tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Using `HostedCodeInterpreterTool` with `ChatClientAgent`
+- Extracting code input and output from agent responses
+- Handling code interpreter annotations and file citations
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -29,5 +29,5 @@
      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
    </None>
  </ItemGroup>
-  
+
 </Project>
@@ -1,11 +1,11 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use Computer Use Tool with AI Agents.
+// This sample shows how to use Computer Use Tool with a ChatClientAgent.

 using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
 using Microsoft.Extensions.AI;
 using OpenAI.Responses;

@@ -15,59 +15,32 @@ internal sealed class Program
 {
    private static async Task Main(string[] args)
    {
-        string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-        string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "computer-use-preview";
-
-        // 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.
-        // Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
-        AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
        const string AgentInstructions = @"
                    You are a computer automation assistant. 
                    
                    Be direct and efficient. When you reach the search results page, read and describe the actual search result titles and descriptions you can see.
                ";

-        const string AgentNameMEAI = "ComputerAgent-MEAI";
-        const string AgentNameNative = "ComputerAgent-NATIVE";
+        const string AgentName = "ComputerAgent-RAPI";

-        // Option 1 - Using ComputerUseTool + AgentOptions (MEAI + AgentFramework)
-        // Create AIAgent directly
-        AIAgent agentOption1 = await aiProjectClient.CreateAIAgentAsync(
-            name: AgentNameMEAI,
-            model: deploymentName,
+        string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+        string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "computer-use-preview";
+
+        // 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.
+        AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+        // Create a AIAgent with ComputerUseTool.
+        AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
            instructions: AgentInstructions,
+            name: AgentName,
            description: "Computer automation agent with screen interaction capabilities.",
            tools: [
-                    ResponseTool.CreateComputerTool(ComputerToolEnvironment.Browser, 1026, 769).AsAITool(),
+                    FoundryAITool.CreateComputerTool(ComputerToolEnvironment.Browser, 1026, 769),
                ]);

-        // Option 2 - Using PromptAgentDefinition SDK native type
-        // Create the server side agent version
-        AIAgent agentOption2 = await aiProjectClient.CreateAIAgentAsync(
-            name: AgentNameNative,
-            creationOptions: new AgentVersionCreationOptions(
-                new PromptAgentDefinition(model: deploymentName)
-                {
-                    Instructions = AgentInstructions,
-                    Tools = { ResponseTool.CreateComputerTool(
-                environment: new ComputerToolEnvironment("windows"),
-                displayWidth: 1026,
-                displayHeight: 769) }
-                })
-        );
-
-        // Either invoke option1 or option2 agent, should have same result
-        // Option 1
-        await InvokeComputerUseAgentAsync(agentOption1);
-
-        // Option 2
-        //await InvokeComputerUseAgentAsync(agentOption2);
-
-        // Cleanup by agent name removes the agent version created.
-        await aiProjectClient.Agents.DeleteAgentAsync(agentOption1.Name);
-        await aiProjectClient.Agents.DeleteAgentAsync(agentOption2.Name);
+        await InvokeComputerUseAgentAsync(agent);
    }

    private static async Task InvokeComputerUseAgentAsync(AIAgent agent)
@@ -94,10 +67,8 @@ internal sealed class Program
        // Initial request with screenshot - start with Bing search page
        Console.WriteLine("Starting computer automation session (initial screenshot: cua_browser_search.png)...");

-        // IMPORTANT: Computer-use with the Azure Agents API differs from the vanilla OpenAI Responses API.
-        // The Azure Agents API rejects requests that include previous_response_id alongside
-        // computer_call_output items. To work around this, each call uses a fresh session (avoiding
-        // previous_response_id) and re-sends the full conversation context as input items instead.
+        // We use PreviousResponseId to chain calls, sending only the new computer_call_output items
+        // instead of re-sending the full context.
        AgentSession session = await agent.CreateSessionAsync();
        AgentResponse response = await agent.RunAsync(message, session: session, options: runOptions);

@@ -161,31 +132,15 @@ internal sealed class Program

            Console.WriteLine("Sending action result back to agent...");

-            // Build the follow-up messages with full conversation context.
-            // The Azure Agents API rejects previous_response_id when computer_call_output items are
-            // present, so we must re-send all prior output items (reasoning, computer_call, etc.)
-            // as input items alongside the computer_call_output to maintain conversation continuity.
-            List<ChatMessage> followUpMessages = [];
-
-            // Re-send all response output items as an assistant message so the API has full context
-            List<AIContent> priorOutputContents = response.Messages
-                .SelectMany(m => m.Contents)
-                .ToList();
-            followUpMessages.Add(new ChatMessage(ChatRole.Assistant, priorOutputContents));
-
-            // Add the computer_call_output as a user message
+            // Send only the computer_call_output — the session carries PreviousResponseId for context continuity.
            AIContent callOutput = new()
            {
                RawRepresentation = new ComputerCallOutputResponseItem(
                    currentCallId,
                    output: ComputerCallOutput.CreateScreenshotOutput(new BinaryData(screenInfo.ImageBytes), "image/png"))
            };
-            followUpMessages.Add(new ChatMessage(ChatRole.User, [callOutput]));

-            // Create a fresh session so ConversationId does not carry over a previous_response_id.
-            // Without this, the Azure Agents API returns an error when computer_call_output is present.
-            session = await agent.CreateSessionAsync();
-            response = await agent.RunAsync(followUpMessages, session: session, options: runOptions);
+            response = await agent.RunAsync([new ChatMessage(ChatRole.User, [callOutput])], session: session, options: runOptions);
        }
    }
 }
@@ -0,0 +1,29 @@
+# Computer Use with the Responses API
+
+This sample shows how to use the Computer Use tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Using `FoundryAITool.CreateComputerTool()` with `ChatClientAgent`
+- Processing computer call actions (click, type, key press)
+- Managing the computer use interaction loop with screenshots
+- Handling the Azure Agents API workaround for `previous_response_id` with `computer_call_output`
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="computer-use-preview"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -9,7 +9,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
    <PackageReference Include="Azure.Identity" />
  </ItemGroup>

@@ -1,22 +1,20 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use File Search Tool with AI Agents.
+// This sample shows how to use File Search Tool with a ChatClientAgent.

 using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using OpenAI.Assistants;
 using OpenAI.Files;
-using OpenAI.Responses;

 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

 const string AgentInstructions = "You are a helpful assistant that can search through uploaded files to answer questions.";

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// We need the AIProjectClient to upload files and create vector stores.
 // 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.
@@ -52,8 +50,11 @@ var vectorStoreResult = await vectorStoresClient.CreateVectorStoreAsync(
 string vectorStoreId = vectorStoreResult.Value.Id;
 Console.WriteLine($"Created vector store, vector store ID: {vectorStoreId}");

-AIAgent agent = await CreateAgentWithMEAI();
-// AIAgent agent = await CreateAgentWithNativeSDK();
+// Create a AIAgent with HostedFileSearchTool.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: "FileSearchAgent-RAPI",
+    tools: [new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreId)] }]);

 // Run the agent
 Console.WriteLine("\n--- Running File Search Agent ---");
@@ -73,39 +74,9 @@ foreach (AIAnnotation annotation in response.Messages.SelectMany(m => m.Contents
    }
 }

-// Cleanup.
+// Cleanup file resources.
 Console.WriteLine("\n--- Cleanup ---");
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
 await vectorStoresClient.DeleteVectorStoreAsync(vectorStoreId);
 await filesClient.DeleteFileAsync(uploadedFile.Id);
 File.Delete(searchFilePath);
 Console.WriteLine("Cleanup completed successfully.");
-
-// --- Agent Creation Options ---
-
-#pragma warning disable CS8321 // Local function is declared but never used
-// Option 1 - Using HostedFileSearchTool (MEAI + AgentFramework)
-async Task<AIAgent> CreateAgentWithMEAI()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        model: deploymentName,
-        name: "FileSearchAgent-MEAI",
-        instructions: AgentInstructions,
-        tools: [new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreId)] }]);
-}
-
-// Option 2 - Using PromptAgentDefinition with ResponseTool.CreateFileSearchTool (Native SDK)
-async Task<AIAgent> CreateAgentWithNativeSDK()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        name: "FileSearchAgent-NATIVE",
-        creationOptions: new AgentVersionCreationOptions(
-            new PromptAgentDefinition(model: deploymentName)
-            {
-                Instructions = AgentInstructions,
-                Tools = {
-                    ResponseTool.CreateFileSearchTool(vectorStoreIds: [vectorStoreId])
-                }
-            })
-    );
-}
@@ -0,0 +1,29 @@
+# File Search with the Responses API
+
+This sample shows how to use the File Search tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Uploading files and creating vector stores via `AIProjectClient`
+- Using `HostedFileSearchTool` with `ChatClientAgent`
+- Handling file citation annotations in agent responses
+- Cleaning up file resources after use
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -6,15 +6,14 @@

    <Nullable>enable</Nullable>
    <ImplicitUsings>enable</ImplicitUsings>
-    <NoWarn>$(NoWarn);IDE0059</NoWarn>
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
    <PackageReference Include="Azure.Identity" />
  </ItemGroup>

  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
  </ItemGroup>

@@ -6,16 +6,32 @@ using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
+using Microsoft.Agents.AI.AzureAI;
+using Microsoft.Extensions.AI;

-// Warning: DefaultAzureCredential is intended for simplicity in development. For production scenarios, consider using a more specific credential.
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

 const string AgentInstructions = "You are a helpful assistant that can use the countries API to retrieve information about countries by their currency code.";
+// 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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// A simple OpenAPI specification for the REST Countries API
-const string CountriesOpenApiSpec = """
+AITool openApiTool = FoundryAITool.CreateOpenApiTool(CreateOpenAPIFunctionDefinition());
+
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: "OpenAPIToolsAgent",
+    tools: [openApiTool]);
+
+// Run the agent with a question about countries
+Console.WriteLine(await agent.RunAsync("What countries use the Euro (EUR) as their currency? Please list them."));
+
+OpenApiFunctionDefinition CreateOpenAPIFunctionDefinition()
+{
+    // A simple OpenAPI specification for the REST Countries API
+    const string CountriesOpenApiSpec = """
 {
  "openapi": "3.1.0",
  "info": {
@@ -68,49 +84,12 @@ const string CountriesOpenApiSpec = """
 }
 """;

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-// Create the OpenAPI function definition
-var openApiFunction = new OpenApiFunctionDefinition(
-    "get_countries",
-    BinaryData.FromString(CountriesOpenApiSpec),
-    new OpenAPIAnonymousAuthenticationDetails())
-{
-    Description = "Retrieve information about countries by currency code"
-};
-
-AIAgent agent = await CreateAgentWithMEAI();
-// AIAgent agent = await CreateAgentWithNativeSDK();
-
-// Run the agent with a question about countries
-Console.WriteLine(await agent.RunAsync("What countries use the Euro (EUR) as their currency? Please list them."));
-
-// Cleanup by deleting the agent
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-
-// --- Agent Creation Options ---
-
-// Option 1 - Using AsAITool wrapping for OpenApiTool (MEAI + AgentFramework)
-async Task<AIAgent> CreateAgentWithMEAI()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        model: deploymentName,
-        name: "OpenAPIToolsAgent-MEAI",
-        instructions: AgentInstructions,
-        tools: [((ResponseTool)AgentTool.CreateOpenApiTool(openApiFunction)).AsAITool()]);
-}
-
-// Option 2 - Using PromptAgentDefinition with AgentTool.CreateOpenApiTool (Native SDK)
-async Task<AIAgent> CreateAgentWithNativeSDK()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        name: "OpenAPIToolsAgent-NATIVE",
-        creationOptions: new AgentVersionCreationOptions(
-            new PromptAgentDefinition(model: deploymentName)
-            {
-                Instructions = AgentInstructions,
-                Tools = { (ResponseTool)AgentTool.CreateOpenApiTool(openApiFunction) }
-            })
-    );
+    // Create the OpenAPI function definition
+    return new(
+        "get_countries",
+        BinaryData.FromString(CountriesOpenApiSpec),
+        new OpenAPIAnonymousAuthenticationDetails())
+    {
+        Description = "Retrieve information about countries by currency code"
+    };
 }
@@ -0,0 +1,29 @@
+# OpenAPI Tools with the Responses API
+
+This sample shows how to use OpenAPI tools with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Defining an OpenAPI specification inline
+- Creating an `OpenAPIFunctionDefinition` for the REST Countries API
+- Using `FoundryAITool.CreateOpenApiTool()` with `ChatClientAgent`
+- Server-side execution of OpenAPI tool calls
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -9,7 +9,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
    <PackageReference Include="Azure.Identity" />
  </ItemGroup>

@@ -1,15 +1,13 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use Bing Custom Search Tool with AI Agents.
+// This sample shows how to use Bing Custom Search Tool with a ChatClientAgent.

 using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
+using Microsoft.Agents.AI.AzureAI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
 string connectionId = Environment.GetEnvironmentVariable("AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID") ?? throw new InvalidOperationException("AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID is not set.");
 string instanceName = Environment.GetEnvironmentVariable("AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME") ?? throw new InvalidOperationException("AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME is not set.");

@@ -18,19 +16,24 @@ const string AgentInstructions = """
    Use the available Bing Custom Search tools to answer questions and perform tasks.
    """;

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// Bing Custom Search tool parameters
+BingCustomSearchToolOptions bingCustomSearchToolParameters = new([
+    new BingCustomSearchConfiguration(connectionId, instanceName)
+]);
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Bing Custom Search tool parameters shared by both options
-BingCustomSearchToolOptions bingCustomSearchToolParameters = new([
-    new BingCustomSearchConfiguration(connectionId, instanceName)
-]);
-
-AIAgent agent = await CreateAgentWithMEAIAsync();
-// AIAgent agent = await CreateAgentWithNativeSDKAsync();
+// Create a AIAgent with Bing Custom Search tool.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: "BingCustomSearchAgent-RAPI",
+    tools: [FoundryAITool.CreateBingCustomSearchTool(bingCustomSearchToolParameters)]);

 Console.WriteLine($"Created agent: {agent.Name}");

@@ -42,35 +45,3 @@ foreach (var message in response.Messages)
 {
    Console.WriteLine(message.Text);
 }
-
-// Cleanup by deleting the agent
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-Console.WriteLine($"\nDeleted agent: {agent.Name}");
-
-// --- Agent Creation Options ---
-
-// Option 1 - Using AsAITool wrapping for the ResponseTool returned by AgentTool.CreateBingCustomSearchTool (MEAI + AgentFramework)
-async Task<AIAgent> CreateAgentWithMEAIAsync()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        model: deploymentName,
-        name: "BingCustomSearchAgent-MEAI",
-        instructions: AgentInstructions,
-        tools: [((ResponseTool)AgentTool.CreateBingCustomSearchTool(bingCustomSearchToolParameters)).AsAITool()]);
-}
-
-// Option 2 - Using PromptAgentDefinition with AgentTool.CreateBingCustomSearchTool (Native SDK)
-async Task<AIAgent> CreateAgentWithNativeSDKAsync()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        name: "BingCustomSearchAgent-NATIVE",
-        creationOptions: new AgentVersionCreationOptions(
-            new PromptAgentDefinition(model: deploymentName)
-            {
-                Instructions = AgentInstructions,
-                Tools = {
-                    (ResponseTool)AgentTool.CreateBingCustomSearchTool(bingCustomSearchToolParameters),
-                }
-            })
-    );
-}
@@ -0,0 +1,36 @@
+# Bing Custom Search with the Responses API
+
+This sample shows how to use the Bing Custom Search tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Configuring `BingCustomSearchToolParameters` with connection ID and instance name
+- Using `FoundryAITool.CreateBingCustomSearchTool()` with `ChatClientAgent`
+- Processing search results from agent responses
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+- Bing Custom Search resource configured with a connection ID
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID="your-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/your-bing-connection"
+$env:AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME="your-instance-name"  # The Bing Custom Search configuration name (from Azure portal)
+```
+
+### Finding the connection ID and instance name
+
+- **Connection ID** (`AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID`): The full ARM resource URI including the `/projects/<name>/connections/<connection-name>` segment. Find the connection name in your Foundry project under **Management center** → **Connected resources**.
+- **Instance Name** (`AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME`): The **configuration name** from your Bing Custom Search resource (Azure portal → your Bing Custom Search resource → **Configurations**). This is _not_ the Azure resource name or the connection name — it's the name of the specific search configuration that defines which domains/sites to search against.
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -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="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,15 +1,13 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use SharePoint Grounding Tool with AI Agents.
+// This sample shows how to use SharePoint Grounding Tool with a ChatClientAgent.

 using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
+using Microsoft.Agents.AI.AzureAI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
 string sharepointConnectionId = Environment.GetEnvironmentVariable("SHAREPOINT_PROJECT_CONNECTION_ID") ?? throw new InvalidOperationException("SHAREPOINT_PROJECT_CONNECTION_ID is not set.");

 const string AgentInstructions = """
@@ -17,18 +15,23 @@ const string AgentInstructions = """
    Use the available SharePoint tools to answer questions and perform tasks.
    """;

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// Create SharePoint tool options with project connection
+var sharepointOptions = new SharePointGroundingToolOptions();
+sharepointOptions.ProjectConnections.Add(new ToolProjectConnection(sharepointConnectionId));
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Create SharePoint tool options with project connection
-var sharepointOptions = new SharePointGroundingToolOptions();
-sharepointOptions.ProjectConnections.Add(new ToolProjectConnection(sharepointConnectionId));
-
-AIAgent agent = await CreateAgentWithMEAIAsync();
-// AIAgent agent = await CreateAgentWithNativeSDKAsync();
+// Create a AIAgent with SharePoint tool.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: "SharePointAgent-RAPI",
+    tools: [FoundryAITool.CreateSharepointTool(sharepointOptions)]);

 Console.WriteLine($"Created agent: {agent.Name}");

@@ -52,33 +55,3 @@ foreach (var message in response.Messages)
        }
    }
 }
-
-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-Console.WriteLine($"\nDeleted agent: {agent.Name}");
-
-// --- Agent Creation Options ---
-
-// Option 1 - Using AgentTool.CreateSharepointTool + AsAITool() (MEAI + AgentFramework)
-async Task<AIAgent> CreateAgentWithMEAIAsync()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        model: deploymentName,
-        name: "SharePointAgent-MEAI",
-        instructions: AgentInstructions,
-        tools: [((ResponseTool)AgentTool.CreateSharepointTool(sharepointOptions)).AsAITool()]);
-}
-
-// Option 2 - Using PromptAgentDefinition SDK native type
-async Task<AIAgent> CreateAgentWithNativeSDKAsync()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        name: "SharePointAgent-NATIVE",
-        creationOptions: new AgentVersionCreationOptions(
-            new PromptAgentDefinition(model: deploymentName)
-            {
-                Instructions = AgentInstructions,
-                Tools = { AgentTool.CreateSharepointTool(sharepointOptions) }
-            })
-    );
-}
@@ -0,0 +1,30 @@
+# SharePoint Grounding with the Responses API
+
+This sample shows how to use the SharePoint Grounding tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Configuring `SharePointGroundingToolOptions` with project connections
+- Using `FoundryAITool.CreateSharepointTool()` with `ChatClientAgent`
+- Displaying grounding annotations from agent responses
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+- SharePoint connection configured in your Microsoft Foundry project
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:SHAREPOINT_PROJECT_CONNECTION_ID="your-sharepoint-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/SharepointTestTool"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -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="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,42 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use Microsoft Fabric Tool with a ChatClientAgent.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
+
+string fabricConnectionId = Environment.GetEnvironmentVariable("FABRIC_PROJECT_CONNECTION_ID") ?? throw new InvalidOperationException("FABRIC_PROJECT_CONNECTION_ID is not set.");
+
+const string AgentInstructions = "You are a helpful assistant with access to Microsoft Fabric data. Answer questions based on data available through your Fabric connection.";
+
+// Configure Microsoft Fabric tool options with project connection
+var fabricToolOptions = new FabricDataAgentToolOptions();
+fabricToolOptions.ProjectConnections.Add(new ToolProjectConnection(fabricConnectionId));
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a AIAgent with Microsoft Fabric tool.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: "FabricAgent-RAPI",
+    tools: [FoundryAITool.CreateMicrosoftFabricTool(fabricToolOptions)]);
+
+Console.WriteLine($"Created agent: {agent.Name}");
+
+// Run the agent with a sample query
+AgentResponse response = await agent.RunAsync("What data is available in the connected Fabric workspace?");
+
+Console.WriteLine("\n=== Agent Response ===");
+foreach (var message in response.Messages)
+{
+    Console.WriteLine(message.Text);
+}
@@ -0,0 +1,30 @@
+# Microsoft Fabric with the Responses API
+
+This sample shows how to use the Microsoft Fabric tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Configuring `FabricDataAgentToolOptions` with project connections
+- Using `FoundryAITool.CreateMicrosoftFabricTool()` with `ChatClientAgent`
+- Querying data available through a Fabric connection
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+- Microsoft Fabric connection configured in your Microsoft Foundry project
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:FABRIC_PROJECT_CONNECTION_ID="your-fabric-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/FabricTestTool"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -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="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use the Web Search Tool with a ChatClientAgent.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI.Responses;
+
+const string AgentInstructions = "You are a helpful assistant that can search the web to find current information and answer questions accurately.";
+const string AgentName = "WebSearchAgent-RAPI";
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a AIAgent with HostedWebSearchTool.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: AgentName,
+    tools: [new HostedWebSearchTool()]);
+
+AgentResponse response = await agent.RunAsync("What's the weather today in Seattle?");
+
+// Get the text response
+Console.WriteLine($"Response: {response.Text}");
+
+// Getting any annotations/citations generated by the web search tool
+foreach (AIAnnotation annotation in response.Messages.SelectMany(m => m.Contents).SelectMany(c => c.Annotations ?? []))
+{
+    Console.WriteLine($"Annotation: {annotation}");
+    if (annotation.RawRepresentation is UriCitationMessageAnnotation urlCitation)
+    {
+        Console.WriteLine($$"""
+            Title: {{urlCitation.Title}}
+            URL: {{urlCitation.Uri}}
+            """);
+    }
+}
@@ -0,0 +1,28 @@
+# Web Search with the Responses API
+
+This sample shows how to use the Web Search tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Using `HostedWebSearchTool` with `ChatClientAgent`
+- Processing web search citations and annotations
+- Extracting URL citation details (title, URL) from responses
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -9,6 +9,8 @@ using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
+using Microsoft.Extensions.AI;
 using OpenAI.Responses;

 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
@@ -22,27 +24,25 @@ const string AgentInstructions = """
    When a user shares personal details or preferences, remember them for future conversations.
    """;

-const string AgentNameMEAI = "MemorySearchAgent-MEAI";
-const string AgentNameNative = "MemorySearchAgent-NATIVE";
+const string AgentName = "MemorySearchAgent";

-// Scope identifies the user or context for memory isolation.
-// Using a unique user identifier ensures memories are private to that user.
 string userScope = $"user_{Environment.MachineName}";

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
-DefaultAzureCredential credential = new();
-AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+MemorySearchPreviewTool memorySearchTool = new(memoryStoreName, userScope) { UpdateDelayInSecs = 0 };
+// 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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create agent using the RAPI path with the MemorySearch tool
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: AgentInstructions,
+    name: AgentName,
+    tools: [FoundryAITool.FromResponseTool(memorySearchTool)]);

 // Ensure the memory store exists and has memories to retrieve.
 await EnsureMemoryStoreAsync();

-// Create the Memory Search tool configuration
-MemorySearchPreviewTool memorySearchTool = new(memoryStoreName, userScope) { UpdateDelayInSecs = 0 };
-
-// Create agent using Option 1 (MEAI) or Option 2 (Native SDK)
-AIAgent agent = await CreateAgentWithMEAI();
-// AIAgent agent = await CreateAgentWithNativeSDK();
-
 try
 {
    Console.WriteLine("Agent created with Memory Search tool. Starting conversation...\n");
@@ -73,41 +73,14 @@ try
 }
 finally
 {
-    // Cleanup: Delete the agent and memory store.
+    // Cleanup: Delete the memory store (no server-side agent to clean up in RAPI path).
    Console.WriteLine("\nCleaning up...");
-    await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-    Console.WriteLine("Agent deleted.");
    await aiProjectClient.MemoryStores.DeleteMemoryStoreAsync(memoryStoreName);
    Console.WriteLine("Memory store deleted.");
 }

-#pragma warning disable CS8321 // Local function is declared but never used
-
-// Option 1 - Using MemorySearchTool wrapped as MEAI AITool
-async Task<AIAgent> CreateAgentWithMEAI()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        model: deploymentName,
-        name: AgentNameMEAI,
-        instructions: AgentInstructions,
-        tools: [((ResponseTool)memorySearchTool).AsAITool()]);
-}
-
-// Option 2 - Using PromptAgentDefinition with MemorySearchTool (Native SDK)
-async Task<AIAgent> CreateAgentWithNativeSDK()
-{
-    return await aiProjectClient.CreateAIAgentAsync(
-        name: AgentNameNative,
-        creationOptions: new AgentVersionCreationOptions(
-            new PromptAgentDefinition(model: deploymentName)
-            {
-                Instructions = AgentInstructions,
-                Tools = { memorySearchTool }
-            })
-    );
-}
-
 // Helpers — kept at the bottom so the main agent flow above stays clean.
+
 async Task EnsureMemoryStoreAsync()
 {
    Console.WriteLine($"Creating memory store '{memoryStoreName}'...");
@@ -123,19 +96,37 @@ async Task EnsureMemoryStoreAsync()
        Console.WriteLine("Memory store created.");
    }

+    // Explicitly add memories from a simulated prior conversation.
    Console.WriteLine("Storing memories from a prior conversation...");
    MemoryUpdateOptions memoryOptions = new(userScope) { UpdateDelay = 0 };
-    memoryOptions.Items.Add(ResponseItem.CreateUserMessageItem("My name is Alice and I love programming in C#."));
+    memoryOptions.Items.Add(ResponseItem.CreateUserMessageItem("My name is Alice and I prefer C#."));

    MemoryUpdateResult updateResult = await aiProjectClient.MemoryStores.WaitForMemoriesUpdateAsync(
        memoryStoreName: memoryStoreName,
-        pollingInterval: 500,
-        options: memoryOptions);
+        options: memoryOptions,
+        pollingInterval: 500);

    if (updateResult.Status == MemoryStoreUpdateStatus.Failed)
    {
        throw new InvalidOperationException($"Memory update failed: {updateResult.ErrorDetails}");
    }

-    Console.WriteLine($"Memory update completed (status: {updateResult.Status}).\n");
+    Console.WriteLine($"Memory update completed (status: {updateResult.Status}).");
+
+    // Quick verification that memories are searchable.
+    Console.WriteLine("Verifying stored memories...");
+    MemorySearchOptions searchOptions = new(userScope)
+    {
+        Items = { ResponseItem.CreateUserMessageItem("What are Alice's preferences?") }
+    };
+    MemoryStoreSearchResponse searchResult = await aiProjectClient.MemoryStores.SearchMemoriesAsync(
+        memoryStoreName: memoryStoreName,
+        options: searchOptions);
+
+    foreach (var memory in searchResult.Memories)
+    {
+        Console.WriteLine($"  - {memory.MemoryItem.Content}");
+    }
+
+    Console.WriteLine();
 }
@@ -0,0 +1,31 @@
+# Memory Search with the Responses API
+
+This sample demonstrates how to use the Memory Search tool with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Configuring `MemorySearchPreviewTool` with a memory store and user scope
+- Using memory search for cross-conversation recall
+- Inspecting `MemorySearchToolCallResponseItem` results
+- User profile persistence across conversations
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+- A memory store created beforehand via Azure Portal or Python SDK
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:AZURE_AI_MEMORY_STORE_ID="your-memory-store-name"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="ModelContextProtocol" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,24 +1,20 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample demonstrates how to use a local MCP (Model Context Protocol) client with Azure Foundry Agents.
-// The MCP tools are resolved locally by connecting directly to the MCP server via HTTP,
-// and then passed to the Foundry agent as client-side tools.
-// This sample uses the Microsoft Learn MCP endpoint to search documentation.
+// This sample demonstrates how to wrap MCP tools with a DelegatingAIFunction to add custom behavior (e.g., logging).
+// Compare with Step09 which shows basic MCP tool usage without wrapping.
+// The LoggingMcpTool pattern is useful for diagnostics, metering, or adding approval logic around tool calls.

 using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using ModelContextProtocol.Client;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+using SampleApp;

 const string AgentInstructions = "You are a helpful assistant that can help with Microsoft documentation questions. Use the Microsoft Learn MCP tool to search for documentation.";
-const string AgentName = "DocsAgent";
+const string AgentName = "DocsAgent-RAPI";

 // Connect to the MCP server locally via HTTP (Streamable HTTP transport).
-// The MCP server is hosted at Microsoft Learn and provides documentation search capabilities.
 Console.WriteLine("Connecting to MCP server at https://learn.microsoft.com/api/mcp ...");

 await using McpClient mcpClient = await McpClient.CreateAsync(new HttpClientTransport(new()
@@ -34,53 +30,48 @@ Console.WriteLine($"MCP tools available: {string.Join(", ", mcpTools.Select(t =>
 // Wrap each MCP tool with a DelegatingAIFunction to log local invocations.
 List<AITool> wrappedTools = mcpTools.Select(tool => (AITool)new LoggingMcpTool(tool)).ToList();

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-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
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Create the agent with the locally-resolved MCP tools.
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    name: AgentName,
+// Create a AIAgent with the locally-resolved MCP tools.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
    instructions: AgentInstructions,
+    name: AgentName,
    tools: wrappedTools);

 Console.WriteLine($"Agent '{agent.Name}' created successfully.");

-try
-{
-    // First query
-    const string Prompt1 = "How does one create an Azure storage account using az cli?";
-    Console.WriteLine($"\nUser: {Prompt1}\n");
-    AgentResponse response1 = await agent.RunAsync(Prompt1);
-    Console.WriteLine($"Agent: {response1}");
+// First query
+const string Prompt1 = "How does one create an Azure storage account using az cli?";
+Console.WriteLine($"\nUser: {Prompt1}\n");
+AgentResponse response1 = await agent.RunAsync(Prompt1);
+Console.WriteLine($"Agent: {response1}");

-    Console.WriteLine("\n=======================================\n");
+Console.WriteLine("\n=======================================\n");

-    // Second query
-    const string Prompt2 = "What is Microsoft Agent Framework?";
-    Console.WriteLine($"User: {Prompt2}\n");
-    AgentResponse response2 = await agent.RunAsync(Prompt2);
-    Console.WriteLine($"Agent: {response2}");
-}
-finally
-{
-    // Cleanup by removing the agent when done
-    await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-    Console.WriteLine($"\nAgent '{agent.Name}' deleted.");
-}
+// Second query
+const string Prompt2 = "What is Microsoft Agent Framework?";
+Console.WriteLine($"User: {Prompt2}\n");
+AgentResponse response2 = await agent.RunAsync(Prompt2);
+Console.WriteLine($"Agent: {response2}");

-/// <summary>
-/// Wraps an MCP tool to log when it is invoked locally,
-/// confirming that the MCP call is happening client-side.
-/// </summary>
-internal sealed class LoggingMcpTool(AIFunction innerFunction) : DelegatingAIFunction(innerFunction)
+namespace SampleApp
 {
-    protected override ValueTask<object?> InvokeCoreAsync(AIFunctionArguments arguments, CancellationToken cancellationToken)
+    /// <summary>
+    /// Wraps an MCP tool to log when it is invoked locally,
+    /// confirming that the MCP call is happening client-side.
+    /// </summary>
+    internal sealed class LoggingMcpTool(AIFunction innerFunction) : DelegatingAIFunction(innerFunction)
    {
-        Console.WriteLine($"  >> [LOCAL MCP] Invoking tool '{this.Name}' locally...");
-        return base.InvokeCoreAsync(arguments, cancellationToken);
+        protected override ValueTask<object?> InvokeCoreAsync(AIFunctionArguments arguments, CancellationToken cancellationToken)
+        {
+            Console.WriteLine($"  >> [LOCAL MCP] Invoking tool '{this.Name}' locally...");
+            return base.InvokeCoreAsync(arguments, cancellationToken);
+        }
    }
 }
@@ -0,0 +1,29 @@
+# Local MCP with the Responses API
+
+This sample demonstrates how to use a local MCP (Model Context Protocol) client with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Connecting to an MCP server via HTTP (Streamable HTTP transport)
+- Resolving MCP tools locally and wrapping them with logging
+- Using `DelegatingAIFunction` to add custom behavior to MCP tools
+- Passing locally-resolved MCP tools to `ChatClientAgent`
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -0,0 +1,81 @@
+# Getting started with Foundry Agents
+
+These samples demonstrate how to use Azure AI Foundry with Agent Framework.
+
+## Quick start
+
+The simplest way to create a Foundry agent is using the `FoundryAgent` type directly:
+
+```csharp
+FoundryAgent agent = new(
+    new Uri(endpoint),
+    new AzureCliCredential(),
+    model: "gpt-4o-mini",
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
+```
+
+Or using the `AIProjectClient.AsAIAgent(...)` extensions:
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+FoundryAgent agent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Foundry project endpoint
+- Azure CLI installed and authenticated
+
+Set:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+Some samples require extra tool-specific environment variables. See each sample for details.
+
+## Samples
+
+| Sample | Description |
+| --- | --- |
+| [FoundryAgent lifecycle](./Agent_Step00_FoundryAgentLifecycle/) | Create a FoundryAgent directly with endpoint and credentials |
+| [Basics (Responses API)](./Agent_Step01_Basics/) | Create and run an agent using AsAIAgent extensions |
+| [Multi-turn conversation](./Agent_Step02.1_MultiturnConversation/) | Multi-turn using sessions and response ID chaining |
+| [Multi-turn with server conversations](./Agent_Step02.2_MultiturnWithServerConversations/) | Server-side conversations visible in Foundry UI |
+| [Using function tools](./Agent_Step03_UsingFunctionTools/) | Function tools |
+| [Function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/) | Human-in-the-loop approval |
+| [Structured output](./Agent_Step05_StructuredOutput/) | Structured output with JSON schema |
+| [Persisted conversations](./Agent_Step06_PersistedConversations/) | Persisting and resuming conversations |
+| [Observability](./Agent_Step07_Observability/) | OpenTelemetry observability |
+| [Dependency injection](./Agent_Step08_DependencyInjection/) | DI with a hosted service |
+| [Using MCP client as tools](./Agent_Step09_UsingMcpClientAsTools/) | MCP client tools |
+| [Using images](./Agent_Step10_UsingImages/) | Image multi-modality |
+| [Agent as function tool](./Agent_Step11_AsFunctionTool/) | Agent as a function tool for another |
+| [Middleware](./Agent_Step12_Middleware/) | Multiple middleware layers |
+| [Plugins](./Agent_Step13_Plugins/) | Plugins with dependency injection |
+| [Code interpreter](./Agent_Step14_CodeInterpreter/) | Code interpreter tool |
+| [Computer use](./Agent_Step15_ComputerUse/) | Computer use tool |
+| [File search](./Agent_Step16_FileSearch/) | File search tool |
+| [OpenAPI tools](./Agent_Step17_OpenAPITools/) | OpenAPI tools |
+| [Bing custom search](./Agent_Step18_BingCustomSearch/) | Bing Custom Search tool |
+| [SharePoint](./Agent_Step19_SharePoint/) | SharePoint grounding tool |
+| [Microsoft Fabric](./Agent_Step20_MicrosoftFabric/) | Microsoft Fabric tool |
+| [Web search](./Agent_Step21_WebSearch/) | Web search tool |
+| [Memory search](./Agent_Step22_MemorySearch/) | Memory search tool |
+| [Local MCP](./Agent_Step23_LocalMCP/) | Local MCP client with HTTP transport |
+
+## Running the samples
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\FoundryAgent_Step01
+```
@@ -1,100 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess
-// the safety and resilience of an AI model against adversarial attacks.
-//
-// It uses the RedTeam API from Azure.AI.Projects to run automated attack simulations
-// with various attack strategies (encoding, obfuscation, jailbreaks) across multiple
-// risk categories (Violence, HateUnfairness, Sexual, SelfHarm).
-//
-// For more details, see:
-// https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent
-
-using Azure.AI.Projects;
-using Azure.Identity;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-Console.WriteLine("=" + new string('=', 79));
-Console.WriteLine("RED TEAMING EVALUATION SAMPLE");
-Console.WriteLine("=" + new string('=', 79));
-Console.WriteLine();
-
-// Initialize Azure credentials and clients
-// 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 aiProjectClient = new(new Uri(endpoint), credential);
-
-// Configure the target model for red teaming
-AzureOpenAIModelConfiguration targetConfig = new(deploymentName);
-
-// Create the red team run configuration
-RedTeam redTeamConfig = new(targetConfig)
-{
-    DisplayName = "FinancialAdvisor-RedTeam",
-    ApplicationScenario = "A financial advisor assistant that provides general financial advice and information.",
-    NumTurns = 3,
-    RiskCategories =
-    {
-        RiskCategory.Violence,
-        RiskCategory.HateUnfairness,
-        RiskCategory.Sexual,
-        RiskCategory.SelfHarm,
-    },
-    AttackStrategies =
-    {
-        AttackStrategy.Easy,
-        AttackStrategy.Moderate,
-        AttackStrategy.Jailbreak,
-    },
-};
-
-Console.WriteLine($"Target model: {deploymentName}");
-Console.WriteLine("Risk categories: Violence, HateUnfairness, Sexual, SelfHarm");
-Console.WriteLine("Attack strategies: Easy, Moderate, Jailbreak");
-Console.WriteLine($"Simulation turns: {redTeamConfig.NumTurns}");
-Console.WriteLine();
-
-// Submit the red team run to the service
-Console.WriteLine("Submitting red team run...");
-RedTeam redTeamRun = await aiProjectClient.RedTeams.CreateAsync(redTeamConfig, options: null);
-
-Console.WriteLine($"Red team run created: {redTeamRun.Name}");
-Console.WriteLine($"Status: {redTeamRun.Status}");
-Console.WriteLine();
-
-// Poll for completion
-Console.WriteLine("Waiting for red team run to complete (this may take several minutes)...");
-while (redTeamRun.Status != "Completed" && redTeamRun.Status != "Failed" && redTeamRun.Status != "Canceled")
-{
-    await Task.Delay(TimeSpan.FromSeconds(15));
-    redTeamRun = await aiProjectClient.RedTeams.GetAsync(redTeamRun.Name);
-    Console.WriteLine($"  Status: {redTeamRun.Status}");
-}
-
-Console.WriteLine();
-
-if (redTeamRun.Status == "Completed")
-{
-    Console.WriteLine("Red team run completed successfully!");
-    Console.WriteLine();
-    Console.WriteLine("Results:");
-    Console.WriteLine(new string('-', 80));
-    Console.WriteLine($"  Run name:    {redTeamRun.Name}");
-    Console.WriteLine($"  Display name: {redTeamRun.DisplayName}");
-    Console.WriteLine($"  Status:      {redTeamRun.Status}");
-
-    Console.WriteLine();
-    Console.WriteLine("Review the detailed results in the Azure AI Foundry portal:");
-    Console.WriteLine($"  {endpoint}");
-}
-else
-{
-    Console.WriteLine($"Red team run ended with status: {redTeamRun.Status}");
-}
-
-Console.WriteLine();
-Console.WriteLine(new string('=', 80));
@@ -1,101 +0,0 @@
-# Red Teaming with Azure AI Foundry (Classic)
-
-> [!IMPORTANT]
-> This sample uses the **classic Azure AI Foundry** red teaming API (`/redTeams/runs`) via `Azure.AI.Projects`. Results are viewable in the classic Foundry portal experience. The **new Foundry** portal's red teaming feature uses a different evaluation-based API that is not yet available in the .NET SDK.
-
-This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess the safety and resilience of an AI model against adversarial attacks.
-
-## What this sample demonstrates
-
- Configuring a red team run targeting an Azure OpenAI model deployment
- Using multiple `AttackStrategy` options (Easy, Moderate, Jailbreak)
- Evaluating across `RiskCategory` categories (Violence, HateUnfairness, Sexual, SelfHarm)
- Submitting a red team scan and polling for completion
- Reviewing results in the Azure AI Foundry portal
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
- .NET 10 SDK or later
- Azure AI Foundry project (hub and project created)
- Azure OpenAI deployment (e.g., gpt-4o or gpt-4o-mini)
- Azure CLI installed and authenticated (for Azure credential authentication)
-
-### Regional Requirements
-
-Red teaming is only available in regions that support risk and safety evaluators:
- **East US 2**, **Sweden Central**, **US North Central**, **France Central**, **Switzerland West**
-
-### Environment Variables
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project" # Replace with your Azure Foundry project endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
-```
-
-## Run the sample
-
-Navigate to the sample directory and run:
-
-```powershell
-cd dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming
-dotnet run
-```
-
-## Expected behavior
-
-The sample will:
-
-1. Configure a `RedTeam` run targeting the specified model deployment
-2. Define risk categories and attack strategies
-3. Submit the scan to Azure AI Foundry's Red Teaming service
-4. Poll for completion (this may take several minutes)
-5. Display the run status and direct you to the Azure AI Foundry portal for detailed results
-
-## Understanding Red Teaming
-
-### Attack Strategies
-
-| Strategy | Description |
-|----------|-------------|
-| Easy | Simple encoding/obfuscation attacks (ROT13, Leetspeak, etc.) |
-| Moderate | Moderate complexity attacks requiring an LLM for orchestration |
-| Jailbreak | Crafted prompts designed to bypass AI safeguards (UPIA) |
-
-### Risk Categories
-
-| Category | Description |
-|----------|-------------|
-| Violence | Content related to violence |
-| HateUnfairness | Hate speech or unfair content |
-| Sexual | Sexual content |
-| SelfHarm | Self-harm related content |
-
-### Interpreting Results
-
- Results are available in the Azure AI Foundry portal (**classic view** — toggle at top-right) under the red teaming section
- Lower Attack Success Rate (ASR) is better — target ASR < 5% for production
- Review individual attack conversations to understand vulnerabilities
-
-### Current Limitations
-
-> [!NOTE]
-> - The .NET Red Teaming API (`Azure.AI.Projects`) currently supports targeting **model deployments only** via `AzureOpenAIModelConfiguration`. The `AzureAIAgentTarget` type exists in the SDK but is consumed by the **Evaluation Taxonomy** API (`/evaluationtaxonomies`), not by the Red Teaming API (`/redTeams/runs`).
-> - Agent-targeted red teaming with agent-specific risk categories (Prohibited actions, Sensitive data leakage, Task adherence) is documented in the [concept docs](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent) but is not yet available via the public REST API or .NET SDK.
-> - Results from this API appear in the **classic** Azure AI Foundry portal view. The new Foundry portal uses a separate evaluation-based system with `eval_*` identifiers.
-
-## Related Resources
-
- [Azure AI Red Teaming Agent](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent)
- [RedTeam .NET API Reference](https://learn.microsoft.com/dotnet/api/azure.ai.projects.redteam?view=azure-dotnet-preview)
- [Risk and Safety Evaluations](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-metrics-built-in#risk-and-safety-evaluators)
-
-## Next Steps
-
-After running red teaming:
-1. Review attack results and strengthen agent guardrails
-2. Explore the Self-Reflection sample (FoundryAgents_Evaluations_Step02_SelfReflection) for quality assessment
-3. Set up continuous red teaming in your CI/CD pipeline
--- a/Show More
+++ b/Show More