.NET: Align Foundry sample environment variables and credentials. (#6422)

* dotnet: refresh Foundry sample guidance Carry forward the still-relevant sample guidance and Foundry-specific documentation fixes from the old stacked sample migration work, adapted to the current repo layout and policy. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: rename Foundry sample env vars Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: remove persistent provider sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: drop SAMPLE_GUIDELINES.md from this PR Defer the guidelines doc and its cross-link to a follow-on PR to avoid broken-link failures in CI. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: add DefaultAzureCredential warning to remaining samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: address PR review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-06-11 10:26:00 -07:00
parent c9e2a490be
commit c79f886dc3
170 changed files with 525 additions and 517 deletions
@@ -24,7 +24,6 @@
    <File Path="samples/02-agents/AgentProviders/README.md" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_A2A/Agent_With_A2A.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_Anthropic/Agent_With_Anthropic.csproj" />
-    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent/Agent_With_AzureAIAgentsPersistent.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureFoundryModel/Agent_With_AzureFoundryModel.csproj" />
    <Project Path="samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
@@ -50,19 +50,6 @@ internal static class AgentsSamples
            ],
        },

-        new SampleDefinition
-        {
-            Name = "Agent_With_AzureAIAgentsPersistent",
-            ProjectPath = "samples/02-agents/AgentProviders/Agent_With_AzureAIAgentsPersistent",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should contain a joke about a pirate.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
        new SampleDefinition
        {
            Name = "Agent_With_AzureAIProject",
@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.Agents.Persistent" />
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-#pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions
-
-// This sample shows how to create and use a simple AI agent with Microsoft Foundry Agents as the backend.
-
-using Azure.AI.Agents.Persistent;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
-
-const string JokerName = "Joker";
-const string JokerInstructions = "You are good at telling jokes.";
-
-// 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.
-var persistentAgentsClient = new PersistentAgentsClient(endpoint, new DefaultAzureCredential());
-
-// You can create a server side persistent agent with the Azure.AI.Agents.Persistent SDK.
-var agentMetadata = await persistentAgentsClient.Administration.CreateAgentAsync(
-    model: deploymentName,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can retrieve an already created server side persistent agent as an AIAgent.
-AIAgent agent1 = await persistentAgentsClient.GetAIAgentAsync(agentMetadata.Value.Id);
-
-// You can also create a server side persistent agent and return it as an AIAgent directly.
-AIAgent agent2 = await persistentAgentsClient.CreateAIAgentAsync(
-    model: deploymentName,
-    name: JokerName,
-    instructions: JokerInstructions);
-
-// You can then invoke the agent like any other AIAgent.
-AgentSession session = await agent1.CreateSessionAsync();
-Console.WriteLine(await agent1.RunAsync("Tell me a joke about a pirate.", session));
-
-// Cleanup for sample purposes.
-await persistentAgentsClient.Administration.DeleteAgentAsync(agent1.Id);
-await persistentAgentsClient.Administration.DeleteAgentAsync(agent2.Id);
@@ -1,26 +0,0 @@
-# Classic Foundry Agents 
-
-This sample demonstrates how to create an agent using the classic Foundry Agents experience.
-
-# Classic vs New Foundry Agents
-
-Below is a comparison between the classic and new Foundry Agents approaches:
-
-[Migration Guide](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/migrate?view=foundry)
-
-# 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
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
-```
@@ -8,8 +8,8 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string JokerName = "JokerAgent";

@@ -1,4 +1,4 @@
-# New Foundry Agents 
+# New Foundry Agents 

 This sample demonstrates how to create an agent using the new Foundry Agents experience. 

@@ -21,6 +21,6 @@ Before you begin, ensure you have the following prerequisites:
 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
@@ -13,7 +13,7 @@ using OpenAI.Chat;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
-var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "Phi-4-mini-instruct";
+var model = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "Phi-4-mini-instruct";

 // Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Microsoft Foundry.
 var clientOptions = new OpenAIClientOptions() { Endpoint = new Uri(endpoint) };
@@ -1,4 +1,4 @@
-## Overview
+## Overview

 This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.

@@ -13,7 +13,7 @@ Before you begin, ensure you have the following prerequisites:
 - .NET 10 SDK or later
 - Microsoft Foundry resource
 - A model deployment in your Microsoft Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
-so if you want to use a different model, ensure that you set your `AZURE_AI_MODEL_DEPLOYMENT_NAME` environment
+so if you want to use a different model, ensure that you set your `FOUNDRY_MODEL` environment
 variable to the name of your deployed model.
 - An API key or role based authentication to access the Microsoft Foundry resource

@@ -30,5 +30,5 @@ $env:AZURE_OPENAI_ENDPOINT="https://ai-foundry-<myresourcename>.services.ai.azur
 $env:AZURE_OPENAI_API_KEY="************"

 # Optional, defaults to Phi-4-mini-instruct
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="Phi-4-mini-instruct"
+$env:FOUNDRY_MODEL="Phi-4-mini-instruct"
 ```
@@ -16,7 +16,6 @@ See the README.md for each sample for the prerequisites for that sample.
 |---|---|
 |[Creating an AIAgent with A2A](./Agent_With_A2A/)|This sample demonstrates how to create AIAgent for an existing A2A agent.|
 |[Creating an AIAgent with Anthropic](./Agent_With_Anthropic/)|This sample demonstrates how to create an AIAgent using Anthropic Claude models as the underlying inference service|
-|[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
 |[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
 |[Creating an AIAgent with Foundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Microsoft Foundry to create an AIAgent|
 |[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
@@ -26,6 +26,9 @@ var skillsProvider = new AgentSkillsProvider(
    SubprocessScriptRunner.RunAsync);

 // --- Agent Setup ---
+// 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 AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -67,6 +67,9 @@ var unitConverterSkill = new AgentInlineSkill(
 var skillsProvider = new AgentSkillsProvider(unitConverterSkill);

 // --- Agent Setup ---
+// 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 AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -22,6 +22,9 @@ var unitConverter = new UnitConverterSkill();
 var skillsProvider = new AgentSkillsProvider(unitConverter);

 // --- Agent Setup ---
+// 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 AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -64,6 +64,9 @@ var skillsProvider = new AgentSkillsProviderBuilder()
    .Build();

 // --- Agent Setup ---
+// 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 AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(new ChatClientAgentOptions
@@ -80,6 +80,9 @@ var weightSkill = new WeightConverterSkill();
 var skillsProvider = new AgentSkillsProvider(distanceSkill, weightSkill);

 // --- Agent Setup ---
+// 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 AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetResponsesClient()
    .AsAIAgent(
@@ -16,6 +16,9 @@ var guestPath = Environment.GetEnvironmentVariable("HYPERLIGHT_PYTHON_GUEST_PATH

 using var codeAct = new HyperlightCodeActProvider(HyperlightCodeActProviderOptions.CreateForWasm(guestPath));

+// 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 AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
@@ -39,6 +39,9 @@ options.Tools = [fetchDocs, queryData, sendEmail];

 using var codeAct = new HyperlightCodeActProvider(options);

+// 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 AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
@@ -31,6 +31,9 @@ var instructions =
    + "and calling `execute_code` instead of computing values yourself.\n\n"
    + executeCode.BuildInstructions(toolsVisibleToModel: false);

+// 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 AzureOpenAIClient(
    new Uri(endpoint),
    new DefaultAzureCredential())
@@ -13,9 +13,9 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;

-string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string foundryEndpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
 string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? "memory-store-sample";
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";

 // Create an AIProjectClient for Foundry with Azure Identity authentication.
@@ -1,4 +1,4 @@
-# Agent with Memory Using Microsoft Foundry
+# Agent with Memory Using Microsoft Foundry

 This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories across sessions.

@@ -22,11 +22,11 @@ This sample demonstrates how to create and run an agent that uses Microsoft Foun

 ```bash
 # Microsoft Foundry project endpoint and memory store name
-export AZURE_AI_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
+export FOUNDRY_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
 export AZURE_AI_MEMORY_STORE_ID="my_memory_store"

 # Model deployment names (models deployed in your Foundry project)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+export FOUNDRY_MODEL="gpt-5.4-mini"
 export AZURE_AI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-ada-002"
 ```

@@ -13,8 +13,8 @@ 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.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // Create an AI Project client and get an OpenAI client that works with the foundry service.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
@@ -10,8 +10,8 @@ using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.Hosting;
 using ModelContextProtocol.Server;

-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-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -1,4 +1,4 @@
-This sample demonstrates how to expose an existing AI agent as an MCP tool.
+This sample demonstrates how to expose an existing AI agent as an MCP tool.

 ## Run the sample

@@ -21,9 +21,9 @@ To use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector)
   ```
 1. Open a web browser and navigate to the URL displayed in the terminal. If not opened automatically, this will open the MCP Inspector interface.
 1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Microsoft Foundry Project to create and run the agent:
-    - AZURE_AI_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
-    - AZURE_AI_MODEL_DEPLOYMENT_NAME = gpt-5.4-mini # Replace with your model deployment name
+    - FOUNDRY_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
+    - FOUNDRY_MODEL = gpt-5.4-mini # Replace with your model deployment name
 1. Find and click the `Connect` button in the MCP Inspector interface to connect to the MCP server.
 1. As soon as the connection is established, open the `Tools` tab in the MCP Inspector interface and select the `Joker` tool from the list.
 1. Specify your prompt as a value for the `query` argument, for example: `Tell me a joke about a pirate` and click the `Run Tool` button to run the tool.
-1. The agent will process the request and return a response in accordance with the provided instructions that instruct it to always start each joke with 'Aye aye, captain!'.
+1. The agent will process the request and return a response in accordance with the provided instructions that instruct it to always start each joke with 'Aye aye, captain!'.
@@ -8,9 +8,9 @@ using Azure.AI.Agents.Persistent;
 using Azure.Identity;
 using Microsoft.Agents.AI;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
 var deepResearchDeploymentName = Environment.GetEnvironmentVariable("AZURE_AI_REASONING_DEPLOYMENT_NAME") ?? "o3-deep-research";
-var modelDeploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var modelDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_AI_BING_CONNECTION_ID") ?? throw new InvalidOperationException("AZURE_AI_BING_CONNECTION_ID is not set.");

 // Configure extended network timeout for long-running Deep Research tasks.
@@ -1,4 +1,4 @@
-# What this sample demonstrates
+# What this sample demonstrates

 This sample demonstrates how to create an Azure AI Agent with the Deep Research Tool, which leverages the o3-deep-research reasoning model to perform comprehensive research on complex topics.

@@ -37,7 +37,7 @@ Set the following environment variables:

 ```powershell
 # Replace with your Microsoft Foundry project endpoint
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"

 # 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>"
@@ -46,4 +46,4 @@ $env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/pr
 $env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"

 # Optional, defaults to gpt-5.4-mini
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
@@ -40,6 +40,9 @@ using OpenAI.Chat;
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";

+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 var chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
    .GetChatClient(deploymentName);

@@ -9,13 +9,16 @@ using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI.Foundry;

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string JokerName = "JokerAgent";

 // Create the AIProjectClient to manage server-side agents.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+// 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 server-side agent version using the native SDK.
 ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
@@ -1,4 +1,4 @@
-# Agent Step 00 - FoundryAgent Lifecycle
+# 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.

@@ -6,14 +6,14 @@ This sample demonstrates the full lifecycle of a `FoundryAgent` backed by a serv

 - A Microsoft Foundry project endpoint
 - A model deployment name (defaults to `gpt-5.4-mini`)
- Azure CLI installed and authenticated
+- An authenticated Azure identity (for example, sign in with `az login`)

 ## 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-5.4-mini`) |
+| `FOUNDRY_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | Yes |
+| `FOUNDRY_MODEL` | Model deployment name | No (defaults to `gpt-5.4-mini`) |

 ## Running the sample

@@ -6,8 +6,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -14,15 +14,15 @@ 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)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**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).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -7,8 +7,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -15,15 +15,15 @@ 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)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**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).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -9,8 +9,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -15,15 +15,15 @@ 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)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**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).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -15,8 +15,8 @@ static string GetWeather([Description("The location to get the weather for.")] s
 // 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -16,15 +16,15 @@ 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)
+- An authenticated Azure identity (for example, sign in with `az login`)

-**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).
+**Note**: This sample uses `DefaultAzureCredential`. `az login` is the easiest local development path, but Visual Studio, VS Code, and managed identity credentials also work when available.

 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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using Microsoft.Extensions.AI;
 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.";

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use function tools that require human-in-the-loo

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using SampleApp;

 #pragma warning disable CA5399

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,13 +12,13 @@ This sample demonstrates how to configure an agent to produce structured output

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -7,8 +7,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to persist and resume agent conversations using ses

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -10,8 +10,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // Create TracerProvider with console exporter.
 string sourceName = Guid.NewGuid().ToString("N");
@@ -13,13 +13,13 @@ This sample demonstrates how to add OpenTelemetry observability to an agent usin

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:APPLICATIONINSIGHTS_CONNECTION_STRING="..."  # Optional
 ```

@@ -9,8 +9,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to register a `ChatClientAgent` in a dependency inj

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -9,8 +9,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-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 ...");
@@ -12,14 +12,14 @@ This sample shows how to use MCP (Model Context Protocol) client tools with a `C

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -7,8 +7,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use image multi-modality with an agent.

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and a vision-capable model deployment (e.g., `gpt-5.4-mini`)
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using Microsoft.Extensions.AI;
 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.";

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use one agent as a function tool for another age

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -20,8 +20,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -14,13 +14,13 @@ This sample demonstrates multiple middleware layers working together: PII filter

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -16,8 +16,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 const string AssistantInstructions = "You are a helpful assistant that helps people find information.";
 const string AssistantName = "PluginAssistant";
@@ -13,13 +13,13 @@ This sample shows how to use plugins with a `ChatClientAgent` using the Response

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using OpenAI.Assistants;
 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,13 +12,13 @@ This sample shows how to use the Code Interpreter tool with a `ChatClientAgent`

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -10,9 +10,12 @@ using Microsoft.Agents.AI.Foundry;
 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.");
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_COMPUTER_USE_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 projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
 using IHostedFileClient fileClient = projectClient.GetProjectOpenAIClient().AsIHostedFileClient();

@@ -1,4 +1,4 @@
-# Computer Use with the Responses API
+# Computer Use with the Responses API

 This sample shows how to use the Computer Use tool with `AIProjectClient.AsAIAgent(...)`.

@@ -39,12 +39,12 @@ The model receives a screenshot as input, analyzes it, and responds with a compu

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
 $env:AZURE_AI_COMPUTER_USE_DEPLOYMENT_NAME="computer-use-preview"
 ```

@@ -9,8 +9,8 @@ using Microsoft.Extensions.AI;
 using OpenAI.Assistants;
 using OpenAI.Files;

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

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

@@ -13,13 +13,13 @@ This sample shows how to use the File Search tool with a `ChatClientAgent` using

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -9,8 +9,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;
 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-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.
@@ -13,13 +13,13 @@ This sample shows how to use OpenAPI tools with a `ChatClientAgent` using the Re

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -21,8 +21,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,14 +12,14 @@ This sample shows how to use the Bing Custom Search tool with a `ChatClientAgent

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-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)
 ```
@@ -19,8 +19,8 @@ const string AgentInstructions = """
 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,14 +12,14 @@ This sample shows how to use the SharePoint Grounding tool with a `ChatClientAge

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:SHAREPOINT_PROJECT_CONNECTION_ID="your-sharepoint-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/SharepointTestTool"
 ```

@@ -16,8 +16,8 @@ const string AgentInstructions = "You are a helpful assistant with access to Mic
 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,14 +12,14 @@ This sample shows how to use the Microsoft Fabric tool with a `ChatClientAgent`

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:FABRIC_PROJECT_CONNECTION_ID="your-fabric-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/FabricTestTool"
 ```

@@ -11,8 +11,8 @@ 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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -12,13 +12,13 @@ This sample shows how to use the Web Search tool with a `ChatClientAgent` using

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -14,8 +14,8 @@ using Microsoft.Agents.AI.Foundry;
 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.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";
 string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? $"foundry-memory-sample-{Guid.NewGuid():N}";

@@ -13,14 +13,14 @@ This sample demonstrates how to use the Memory Search tool with a `ChatClientAge

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:AZURE_AI_MEMORY_STORE_ID="your-memory-store-name"
 ```

@@ -30,8 +30,8 @@ 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();

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
@@ -13,13 +13,13 @@ This sample demonstrates how to use a local MCP (Model Context Protocol) client

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 ## Run the sample
@@ -12,8 +12,8 @@ using Microsoft.Agents.AI;
 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.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "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
@@ -35,13 +35,13 @@ The container ID and file ID are available from the `ContainerFileCitationMessag

 - .NET 10 SDK or later
 - Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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"  # Optional, defaults to gpt-4o-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
 ```

 ## Run the sample
@@ -24,9 +24,9 @@ using OpenAI.Responses;
 const string ToolboxName = "research_toolbox";
 const string Query = "What tools do you have access to?";

-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+    ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 TokenCredential credential = new DefaultAzureCredential();

@@ -12,18 +12,18 @@ This sample shows how to use a Foundry Toolbox by pointing an `McpClient` at the
 ## Prerequisites

 - A Microsoft Foundry project with a toolbox configured (or let the sample create one for you)
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 The sample creates a toolbox named `research_toolbox` in your Foundry project on
 startup, then connects to its MCP endpoint at
-`{AZURE_AI_PROJECT_ENDPOINT}/toolboxes/research_toolbox/mcp?api-version=v{version}`.
+`{FOUNDRY_PROJECT_ENDPOINT}/toolboxes/research_toolbox/mcp?api-version=v{version}`.

 ## Run the sample

@@ -14,9 +14,9 @@ using Microsoft.Agents.AI;
 using ModelContextProtocol.Client;

 // --- Configuration ---
-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-5.4-mini";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+    ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
 string toolboxMcpServerUrl = Environment.GetEnvironmentVariable("FOUNDRY_TOOLBOX_MCP_SERVER_URL")
    ?? throw new InvalidOperationException("FOUNDRY_TOOLBOX_MCP_SERVER_URL is not set.");

@@ -15,13 +15,13 @@ and inject them as `AIContextProviders` so the agent can discover and use them a

 - A Microsoft Foundry project with a toolbox already configured
 - The toolbox MCP endpoint must expose `skill://index.json` with `skill-md` entries (SEP-2640). If the resource is absent, the sample runs but the skills provider will be empty.
- Azure CLI installed and authenticated (`az login`)
+- An authenticated Azure identity (for example, sign in with `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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 $env:FOUNDRY_TOOLBOX_MCP_SERVER_URL="https://your-foundry-service.services.ai.azure.com/api/projects/your-project/toolboxes/your-toolbox/mcp?api-version=v1"
 ```

@@ -4,12 +4,12 @@ These samples demonstrate how to use Microsoft Foundry with Agent Framework.

 ## Quick start

-The simplest way to create a Foundry agent is using the `FoundryAgent` type directly:
+You can create a Foundry agent directly with the `FoundryAgent` type:

 ```csharp
 FoundryAgent agent = new(
    new Uri(endpoint),
-    new AzureCliCredential(),
+    new DefaultAzureCredential(),
    model: "gpt-5.4-mini",
    instructions: "You are good at telling jokes.",
    name: "JokerAgent");
@@ -32,13 +32,13 @@ FoundryAgent agent = aiProjectClient.AsAIAgent(

 - .NET 10 SDK or later
 - Foundry project endpoint
- Azure CLI installed and authenticated
+- An authenticated Azure identity (for example, sign in with `az login`)

 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-5.4-mini"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-5.4-mini"
 ```

 Some samples require extra tool-specific environment variables. See each sample for details.
@@ -78,7 +78,11 @@ Some samples require extra tool-specific environment variables. See each sample

 ## Running the samples

+Use the basics sample for a quick smoke test:
+
 ```powershell
 cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\FoundryAgent_Step01
-```
+dotnet run --project .\Agent_Step01_Basics
+```
+
+If you want to exercise the full create-run-delete lifecycle, run `Agent_Step00_FoundryAgentLifecycle`.
@@ -8,8 +8,8 @@ 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";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "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
@@ -1,4 +1,4 @@
-# Evaluation - Custom Evals
+# Evaluation - Custom Evals

 This sample demonstrates writing custom domain-specific evaluation functions using `FunctionEvaluator.Create`. Custom evaluators run locally with no cloud evaluator service needed — useful for enforcing business rules, format requirements, or safety guardrails.

@@ -13,13 +13,13 @@ This sample demonstrates writing custom domain-specific evaluation functions usi
 ## Prerequisites

 - .NET 10 SDK or later
- Azure CLI installed and authenticated (`az login`)
+- Azure authentication available to `DefaultAzureCredential` (for local development, run `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"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
 ```

 ## Run the sample
@@ -6,10 +6,13 @@ 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";
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";

 // Create a math tutor agent.
+// 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,
@@ -1,4 +1,4 @@
-# Evaluation - Expected Outputs
+# Evaluation - Expected Outputs

 This sample demonstrates evaluating agent responses against expected outputs using built-in checks.

@@ -16,8 +16,8 @@ This sample demonstrates evaluating agent responses against expected outputs usi
 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"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
 ```

 ## Run the sample
@@ -10,8 +10,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI.Evaluation;
 using FoundryEvals = Microsoft.Agents.AI.Foundry.FoundryEvals;

-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 endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "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
@@ -1,4 +1,4 @@
-# Evaluation - Simple Eval
+# Evaluation - Simple Eval

 The simplest agent evaluation: create a Foundry agent, run it against test questions, and use Foundry quality evaluators (Relevance, Coherence) to score the responses.

@@ -11,14 +11,14 @@ The simplest agent evaluation: create a Foundry agent, run it against test quest
 ## Prerequisites

 - .NET 10 SDK or later
- Azure CLI installed and authenticated (`az login`)
+- Azure authentication available to `DefaultAzureCredential` (for local development, run `az login`)
 - A deployed model in your Azure AI 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"
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
 ```

 ## Run the sample
@@ -25,8 +25,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using SampleApp;

-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-5.4";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4";

 const int MaxContextWindowTokens = 1_050_000;
 const int MaxOutputTokens = 128_000;
@@ -1,4 +1,4 @@
-# What this sample demonstrates
+# What this sample demonstrates

 This sample demonstrates how to use a `HarnessAgent` with the Harness `AIContextProviders` (`TodoProvider` and `AgentModeProvider`) for interactive research tasks with web search capabilities powered by Azure AI Foundry. The `HarnessAgent` pre-configures function invocation, per-service-call chat history persistence, and context-window compaction.

@@ -30,7 +30,7 @@ Set the following environment variables:
 export AZURE_FOUNDRY_OPENAI_ENDPOINT="https://your-project.services.ai.azure.com/openai/v1/"

 # Optional: Model deployment name (defaults to gpt-5.4)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4"
+export FOUNDRY_MODEL="gpt-5.4"
 ```

 ## Running the Sample
@@ -20,8 +20,8 @@ using Harness.Shared.Console.OpenAI;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4";

 const int MaxContextWindowTokens = 1_050_000;
 const int MaxOutputTokens = 128_000;
@@ -30,6 +30,9 @@ const string TracingSourceName = "Harness.SubAgents";
 // Set up OpenTelemetry tracing that writes spans to a text file.
 using var tracerProvider = HarnessTracing.CreateFileTracerProvider(TracingSourceName);

+// 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.
 // Create the AIProjectClient for communicating with the Foundry responses service.
 var projectClient = new AIProjectClient(
    new Uri(endpoint),
@@ -1,4 +1,4 @@
-# Harness Step 02 — BackgroundAgents (Stock Price Research)
+# Harness Step 02 — BackgroundAgents (Stock Price Research)

 This sample demonstrates how to use the **BackgroundAgentsProvider** to delegate work from a parent agent to background agents. Both agents use `HarnessAgent` for pre-configured function invocation, per-service-call persistence, and context-window compaction.

@@ -35,7 +35,7 @@ A parent agent receives a list of stock tickers and uses a web-search background
 - An Azure AI Foundry endpoint with an OpenAI model deployment
 - Set the following environment variables:
  - `AZURE_FOUNDRY_OPENAI_ENDPOINT` — Your Foundry OpenAI endpoint URL
-  - `AZURE_AI_MODEL_DEPLOYMENT_NAME` — Model deployment name (defaults to `gpt-5.4`)
+  - `FOUNDRY_MODEL` — Model deployment name (defaults to `gpt-5.4`)

 ## Running the Sample

@@ -22,8 +22,8 @@ using Harness.Shared.Console;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4";

 const int MaxContextWindowTokens = 1_050_000;
 const int MaxOutputTokens = 128_000;
@@ -57,6 +57,9 @@ var instructions =
    - Always explain what you learned and what you are going to do next between tool calls, so the user can follow along with your thought process.
    """;

+// 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.
 // Create the agent using AsHarnessAgent. The FileAccessStore is explicitly set to the
 // sample's working/ folder (copied to the output directory) so it works regardless of cwd.
 // Unused features are disabled.
@@ -1,4 +1,4 @@
-# What this sample demonstrates
+# What this sample demonstrates

 This sample demonstrates how to use a `HarnessAgent` with the default `FileAccessProvider` to give an agent access to a folder of data files for reading, analyzing, and writing results. The `HarnessAgent` pre-configures function invocation, per-service-call chat history persistence, in-loop compaction, tool approval, and OpenTelemetry — so the sample only needs to supply the chat client, token limits, custom instructions, and opt out of unused features.

@@ -27,7 +27,7 @@ Set the following environment variables:
 export AZURE_FOUNDRY_OPENAI_ENDPOINT="https://your-project.services.ai.azure.com/openai/v1/"

 # Optional: Model deployment name (defaults to gpt-5.4)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4"
+export FOUNDRY_MODEL="gpt-5.4"
 ```

 ## Running the Sample
@@ -27,8 +27,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Hyperlight;
 using Microsoft.Extensions.AI;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4";

 const int MaxContextWindowTokens = 1_050_000;
 const int MaxOutputTokens = 128_000;
@@ -78,6 +78,9 @@ var instructions =
    - If applicable, save final results to file memory.
    """;

+// 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.
 // Create the agent with ALL HarnessAgent features enabled plus Hyperlight CodeAct.
 // No Disable* flags are set — TodoProvider, AgentModeProvider, FileMemory, FileAccess,
 // ToolApproval, WebSearch, and AgentSkillsProvider are all active.
@@ -1,4 +1,4 @@
-# Harness Step 04 — Code Execution (Hyperlight + Skills)
+# Harness Step 04 — Code Execution (Hyperlight + Skills)

 This sample demonstrates a HarnessAgent with **all features enabled**, plus:

@@ -17,8 +17,8 @@ The agent can plan tasks, manage modes, store memories, read/write files, search

 | Variable | Description |
 |----------|-------------|
-| `AZURE_AI_PROJECT_ENDPOINT` | Your Azure AI Foundry project endpoint |
-| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Model deployment name (default: `gpt-5.4`) |
+| `FOUNDRY_PROJECT_ENDPOINT` | Your Azure AI Foundry project endpoint |
+| `FOUNDRY_MODEL` | Model deployment name (default: `gpt-5.4`) |

 ## Running

@@ -11,8 +11,8 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 using OpenAI.Responses;

-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var model = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";

 // Get a client to create/retrieve server side agents with.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
@@ -1,4 +1,4 @@
-# Prerequisites
+# Prerequisites

 Before you begin, ensure you have the following prerequisites:

@@ -11,6 +11,6 @@ Before you begin, ensure you have the following prerequisites:
 Set the following environment variables:

 ```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
@@ -23,10 +23,13 @@ public static class Program
    private static async Task Main()
    {
        // Set up the Azure AI Project client
-        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-5.4-mini";
-        var aiProjectClient = new AIProjectClient(new Uri(endpoint), new AzureCliCredential());
+        var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT")
+            ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+        var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+        // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+        // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+        // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+        var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());

        // Create agents
        AIAgent frenchAgent = await CreateTranslationAgentAsync("French", aiProjectClient, deploymentName);
--- a/Show More
+++ b/Show More