Foundry hosted agent responses emit failed events

Python: Integrate shell tool into harness agent (#6451 )
* Integrate shell tool into AgentHarness * Validate shell_executor exposes as_function() with a clear TypeError Addresses PR review feedback: a public factory should fail fast with an actionable error rather than a cryptic AttributeError when an incompatible shell_executor is supplied. Validation happens upfront, regardless of whether the client supports shell tools. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Type shell harness params via TYPE_CHECKING import Addresses PR review feedback: type shell_executor and shell_environment_provider_options instead of Any, using a TYPE_CHECKING import from agent_framework_tools.shell. The import never executes at runtime, so there is no circular dependency, and the lazy runtime import of ShellEnvironmentProvider is retained. Since ShellExecutor is a protocol without as_function(), the validated getattr result is invoked directly. 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 · 2026-06-12 15:07:11 -07:00 · 2026-06-11 20:51:59 +00:00 · 2026-06-11 18:18:05 +00:00 · 2026-06-11 17:35:44 +00:00 · 2026-06-11 17:26:00 +00:00
338 changed files with 13841 additions and 1606 deletions
@@ -88,6 +88,7 @@ jobs:
        env:
          COSMOSDB_ENDPOINT: https://localhost:8081
          COSMOSDB_KEY: C2y6yDjf5/R+ob0N8A7Cgv30VRDJIWEHLM+4QDU5DE2nQ9nDuVTqobD4b8mGGyPMbIZnqyMsEcaGQy67XIw/Jw==
+          COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_GITHUB_TOKEN }}
          OpenAI__ApiKey: ${{ secrets.OPENAI__APIKEY }}
          OpenAI__ChatModelId: ${{ vars.OPENAI__CHATMODELID }}
          OpenAI__ChatReasoningModelId: ${{ vars.OPENAI__CHATREASONINGMODELID }}
@@ -27,7 +27,7 @@ jobs:
          # "Cleanup artifacts", "Agent", "Prepare", and "Upload results" are check runs
          # created by an org-level GitHub App (MSDO), not by any workflow in this repo.
          # They are outside our control and their transient failures should not block merges.
-          IGNORED_NAMES: "CodeQL,CodeQL analysis (csharp),Cleanup artifacts,Agent,Prepare,Upload results"
+          IGNORED_NAMES: "CodeQL,CodeQL analysis (csharp),Cleanup artifacts,Agent,Prepare,Upload results,review"
        with:
          script: |
            const timeoutSeconds = Number(process.env.TIMEOUT_SECONDS);
@@ -206,6 +206,7 @@ temp*/
 .temp/

 # AI
+**/.checkpoints/
 .claude/
 .omc/
 .omx/
@@ -213,6 +214,7 @@ WARP.md
 **/memory-bank/
 **/projectBrief.md
 **/tmpclaude*
+.kiro/
 # Dependency-bound validation reports
 python/scripts/dependency-*-results.json
 python/scripts/dependencies/dependency-*-results.json
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+<path d="M48.6094 179.978V137.487C48.6096 128.382 53.4685 119.976 61.3467 115.428L79.7451 104.813C80.9999 104.087 82.5759 104.992 82.5762 106.445V173.207C82.5762 174.384 83.2036 175.472 84.2227 176.06L125.08 199.647C128.837 201.777 133.533 199.053 133.523 194.718V126.806C133.523 112.388 141.484 99.1684 154.18 92.4135L154.788 92.0971L163.713 87.5473C165.441 86.667 167.489 87.9224 167.489 89.8618L167.488 180.009L167.474 180.86C167.181 189.624 162.399 197.653 154.762 202.065L154.765 202.068C154.615 202.154 154.463 202.237 154.312 202.32L154.468 202.229C154.375 202.281 154.283 202.333 154.189 202.384C154.23 202.362 154.271 202.342 154.312 202.32L120.779 221.679L120.034 222.092C112.534 226.093 103.539 226.092 96.0508 222.095L95.3066 221.682L68.3447 206.115C68.0004 205.916 67.6627 205.707 67.3301 205.494L61.3467 202.039C53.7053 197.624 48.9149 189.595 48.623 180.829L48.6094 179.978ZM175.737 84.5005C175.737 83.3974 175.186 82.3728 174.277 81.7641L174.091 81.6479L133.25 58.0698C129.49 55.9211 124.778 58.6479 124.788 62.9897V130.902L124.782 131.587C124.53 145.966 116.369 159.063 103.523 165.611L94.5986 170.16L94.4355 170.236C92.799 170.938 90.9459 169.803 90.8281 168.026L90.8223 167.846L90.8232 77.6987C90.8241 68.6053 95.6664 60.1948 103.552 55.6411L103.549 55.6401C103.699 55.5536 103.851 55.4714 104.002 55.3882L103.893 55.4516C103.956 55.4159 104.02 55.3794 104.084 55.3442C104.057 55.359 104.029 55.3732 104.002 55.3882L137.533 36.0288C145.424 31.4763 155.13 31.4781 163.007 36.0259L189.969 51.5932C190.313 51.792 190.65 52.0011 190.982 52.2143L196.967 55.6694C204.855 60.2267 209.704 68.6343 209.704 77.73V120.221L209.689 121.073C209.397 129.848 204.599 137.874 196.967 142.28L178.568 152.895C177.314 153.621 175.738 152.716 175.737 151.263V84.5005ZM176.814 149.866L176.819 149.864L176.832 149.856C176.826 149.859 176.82 149.862 176.814 149.866ZM137.023 194.71L137.019 195.038C136.802 201.878 129.341 206.087 123.354 202.692L123.33 202.678L82.4727 179.091C80.3698 177.877 79.0762 175.634 79.0762 173.207V109.239L63.0957 118.458C56.5124 122.259 52.3745 129.183 52.1221 136.752L52.1094 137.487V179.978C52.1094 187.823 56.2909 195.075 63.0957 199.007H63.0967L69.0801 202.462L69.1504 202.503L69.2188 202.547C69.5212 202.741 69.8111 202.92 70.0947 203.083L97.0566 218.651L97.6982 219.007C104.372 222.569 112.434 222.453 119.029 218.648L152.514 199.316L152.512 199.312C152.581 199.274 152.65 199.236 152.752 199.178L152.753 199.181C152.775 199.169 152.796 199.158 152.815 199.147L153.011 199.035C159.81 195.107 163.987 187.854 163.988 180.009V91.3344L156.378 95.2153C144.501 101.27 137.023 113.475 137.023 126.806V194.71ZM94.3223 166.372L101.934 162.493C113.811 156.438 121.288 144.233 121.288 130.902V62.9897C121.278 55.9521 128.901 51.5532 134.986 55.0307L135 55.0385L175.841 78.6167L176.036 78.7339C178.023 79.9714 179.237 82.15 179.237 84.5005V148.468L195.217 139.249C202.013 135.326 206.204 128.075 206.204 120.221V77.73C206.204 69.8848 202.022 62.6318 195.217 58.6997L189.232 55.2456L189.162 55.2046L189.094 55.1606C188.788 54.9649 188.5 54.787 188.219 54.6245L161.257 39.0571C154.463 35.135 146.091 35.1311 139.282 39.0591L139.283 39.06L105.765 58.4106L105.767 58.4135C105.713 58.443 105.723 58.4383 105.602 58.5063L105.6 58.5034C105.535 58.5391 105.481 58.5691 105.433 58.5962L105.302 58.6723C98.5016 62.5995 94.324 69.8532 94.3232 77.6987L94.3223 166.372Z" fill="white"/>
+</svg>
@@ -1125,7 +1125,7 @@ Naming (Python): N/A (Composable Components)
 Supports: N  
 Observation: No explicit middleware/filters; modularity allows composable units but no dedicated interception hooks or callbacks for custom reading/modification mid-execution.

-For more details, see the official documentation: [Atomic Agents Docs](https://brainblend-ai.github.io/atomic-agents/). No specific code examples available for interception.
+No specific code examples available for interception.

 #### Smolagents (Hugging Face)

@@ -41,19 +41,19 @@
    <!-- Newtonsoft.Json -->
    <PackageVersion Include="Newtonsoft.Json" Version="13.0.4" />
    <!-- System.* -->
-    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.6" />
+    <PackageVersion Include="Microsoft.Bcl.AsyncInterfaces" Version="10.0.8" />
    <PackageVersion Include="Microsoft.Bcl.HashCode" Version="6.0.0" />
    <PackageVersion Include="Microsoft.Bcl.Memory" Version="10.0.5" />
    <PackageVersion Include="System.ClientModel" Version="1.12.0" />
    <PackageVersion Include="System.CodeDom" Version="10.0.0" />
    <PackageVersion Include="System.Collections.Immutable" Version="10.0.1" />
    <PackageVersion Include="System.CommandLine" Version="2.0.0-rc.2.25502.107" />
-    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.6" />
+    <PackageVersion Include="System.Diagnostics.DiagnosticSource" Version="10.0.8" />
    <PackageVersion Include="System.Linq.AsyncEnumerable" Version="10.0.5" />
    <PackageVersion Include="System.Net.Http.Json" Version="10.0.0" />
    <PackageVersion Include="System.Net.ServerSentEvents" Version="10.0.5" />
-    <PackageVersion Include="System.Text.Json" Version="10.0.6" />
-    <PackageVersion Include="System.Threading.Channels" Version="10.0.6" />
+    <PackageVersion Include="System.Text.Json" Version="10.0.8" />
+    <PackageVersion Include="System.Threading.Channels" Version="10.0.8" />
    <PackageVersion Include="System.Threading.Tasks.Extensions" Version="4.6.3" />
    <PackageVersion Include="System.Net.Security" Version="4.3.2" />
    <!-- OpenTelemetry -->
@@ -72,12 +72,12 @@
    <PackageVersion Include="Microsoft.AspNetCore.OpenApi" Version="10.0.0" />
    <PackageVersion Include="Swashbuckle.AspNetCore.SwaggerUI" Version="10.0.0" />
    <!-- Microsoft.Extensions.* -->
-    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.5.1" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.5.1" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation" Version="10.4.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Quality" Version="10.4.0" />
-    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Safety" Version="10.3.0-preview.1.26109.11" />
-    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.5.1" />
+    <PackageVersion Include="Microsoft.Extensions.AI" Version="10.6.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Abstractions" Version="10.6.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation" Version="10.6.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Quality" Version="10.6.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.Evaluation.Safety" Version="10.6.0" />
+    <PackageVersion Include="Microsoft.Extensions.AI.OpenAI" Version="10.6.0" />
    <PackageVersion Include="Microsoft.Extensions.Caching.Memory" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Compliance.Abstractions" Version="10.5.0" />
    <PackageVersion Include="Microsoft.Extensions.Configuration" Version="10.0.1" />
@@ -86,12 +86,12 @@
    <PackageVersion Include="Microsoft.Extensions.Configuration.Json" Version="10.0.1" />
    <PackageVersion Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.1" />
    <PackageVersion Include="Microsoft.Extensions.DependencyInjection" Version="10.0.1" />
-    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.6" />
+    <PackageVersion Include="Microsoft.Extensions.DependencyInjection.Abstractions" Version="10.0.8" />
    <PackageVersion Include="Microsoft.Extensions.FileSystemGlobbing" Version="10.0.6" />
    <PackageVersion Include="Microsoft.Extensions.Hosting" Version="10.0.1" />
    <PackageVersion Include="Microsoft.Extensions.Http.Resilience" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.Logging" Version="10.0.1" />
-    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.6" />
+    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="10.0.8" />
    <PackageVersion Include="Microsoft.Extensions.Logging.Console" Version="10.0.1" />
    <PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="10.0.0" />
    <PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.7.0" />
@@ -138,10 +138,15 @@
    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" Version="2.1.0" />
    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Mcp" Version="1.0.0" />
    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Sdk" Version="2.0.7" />
+    <!-- Valkey -->
    <!-- Redis -->
    <PackageVersion Include="StackExchange.Redis" Version="2.10.1" />
+    <!-- Valkey -->
+    <PackageVersion Include="Valkey.Glide" Version="1.1.0" />
    <!-- Console UX -->
    <PackageVersion Include="Spectre.Console" Version="0.49.1" />
+    <!-- AWS -->
+    <PackageVersion Include="AWSSDK.Extensions.Bedrock.MEAI" Version="4.0.6.10" />
    <!-- Test -->
    <PackageVersion Include="FluentAssertions" Version="8.8.0" />
    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Condition="'$(TargetFramework)' == 'net8.0'" Version="8.0.22" />
@@ -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" />
@@ -129,6 +128,7 @@
    <Project Path="samples/02-agents/Harness/Harness_Step02_Research_WithBackgroundAgents/Harness_Step02_Research_WithBackgroundAgents.csproj" />
    <Project Path="samples/02-agents/Harness/Harness_Step03_DataProcessing/Harness_Step03_DataProcessing.csproj" />
    <Project Path="samples/02-agents/Harness/Harness_Step04_CodeExecution/Harness_Step04_CodeExecution.csproj" />
+    <Project Path="samples/02-agents/Harness/Harness_Step05_Loop/Harness_Step05_Loop.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
    <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
@@ -194,6 +194,8 @@
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step02_MemoryUsingMem0/AgentWithMemory_Step02_MemoryUsingMem0.csproj" />
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/AgentWithMemory_Step04_MemoryUsingFoundry.csproj" />
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step05_BoundedChatHistory/AgentWithMemory_Step05_BoundedChatHistory.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey/AgentWithMemory_Step03_MemoryUsingValkey.csproj" />
+    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock/AgentWithMemory_Step03_MemoryUsingValkey_Bedrock.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/AgentWithOpenAI/">
    <File Path="samples/02-agents/AgentWithOpenAI/README.md" />
@@ -344,6 +346,9 @@
  <Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-Toolbox/">
    <Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-Toolbox/HostedToolbox.csproj" />
  </Folder>
+  <Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-Toolbox-AuthPaths/">
+    <Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-Toolbox-AuthPaths/Hosted-Toolbox-AuthPaths.csproj" />
+  </Folder>
  <Folder Name="/Samples/04-hosting/FoundryHostedAgents/responses/Hosted-ToolboxMcpSkills/">
    <Project Path="samples/04-hosting/FoundryHostedAgents/responses/Hosted-ToolboxMcpSkills/HostedToolboxMcpSkills.csproj" />
  </Folder>
@@ -622,6 +627,7 @@
    <Project Path="src/Microsoft.Agents.AI.Workflows.Generators/Microsoft.Agents.AI.Workflows.Generators.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
    <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Valkey/Microsoft.Agents.AI.Valkey.csproj" />
  </Folder>
  <Folder Name="/Tests/" />
  <Folder Name="/Tests/IntegrationTests/">
@@ -675,5 +681,6 @@
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Generators.UnitTests/Microsoft.Agents.AI.Workflows.Generators.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Valkey.UnitTests/Microsoft.Agents.AI.Valkey.UnitTests.csproj" />
  </Folder>
 </Solution>
@@ -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",
@@ -1,14 +1,14 @@
 <Project>
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
-    <VersionPrefix>1.9.0</VersionPrefix>
+    <VersionPrefix>1.10.0</VersionPrefix>
    <RCNumber>1</RCNumber>
-    <DateSuffix>260603</DateSuffix>
+    <DateSuffix>260610</DateSuffix>
    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).$(DateSuffix).1</PackageVersion>
    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.$(DateSuffix).1</PackageVersion>
    <PackageVersion Condition="'$(IsReleased)' == 'true'">$(VersionPrefix)</PackageVersion>
-    <GitTag>1.9.0</GitTag>
+    <GitTag>1.10.0</GitTag>

    <Configurations>Debug;Release;Publish</Configurations>
    <IsPackable>true</IsPackable>
@@ -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())
@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Valkey\Microsoft.Agents.AI.Valkey.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,55 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates using Valkey for persistent chat history with the Agent Framework.
+// ValkeyChatHistoryProvider persists conversation history across sessions using Valkey lists.
+//
+// Prerequisites:
+//   - A running Valkey server (any version):
+//       docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+//   - Azure OpenAI endpoint and deployment configured via environment variables
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Valkey;
+using Microsoft.Extensions.AI;
+using OpenAI.Chat;
+using Valkey.Glide;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+var valkeyConnection = Environment.GetEnvironmentVariable("VALKEY_CONNECTION") ?? "localhost:6379";
+
+var connection = await ConnectionMultiplexer.ConnectAsync(valkeyConnection);
+
+Console.WriteLine("=== ValkeyChatHistoryProvider — Persistent Chat History ===\n");
+
+var historyProvider = new ValkeyChatHistoryProvider(
+    connection,
+    _ => new ValkeyChatHistoryProvider.State($"sample-{Guid.NewGuid():N}"),
+    new ValkeyChatHistoryProviderOptions
+    {
+        KeyPrefix = "sample_chat",
+        MaxMessages = 20
+    });
+
+AIAgent historyAgent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetChatClient(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions()
+    {
+        ChatOptions = new() { Instructions = "You are a helpful assistant that remembers our conversation." },
+        ChatHistoryProvider = historyProvider
+    });
+
+AgentSession session1 = await historyAgent.CreateSessionAsync();
+Console.WriteLine(await historyAgent.RunAsync("Hello! My name is Alex and I'm a software engineer.", session1));
+Console.WriteLine(await historyAgent.RunAsync("I'm working on a project using Valkey for caching.", session1));
+Console.WriteLine(await historyAgent.RunAsync("What do you remember about me?", session1));
+
+var messageCount = await historyProvider.GetMessageCountAsync(session1);
+Console.WriteLine($"\n  Stored {messageCount} messages in Valkey.\n");
+
+// Clean up
+connection.Dispose();
+
+Console.WriteLine("Done!");
@@ -0,0 +1,30 @@
+# Agent with Memory Using Valkey
+
+This sample demonstrates using Valkey for persistent chat history with the Agent Framework.
+
+## Components
+
+- **ValkeyChatHistoryProvider** — Persists conversation history across sessions using Valkey lists. Works with any Valkey or Redis OSS server (no search module required).
+
+## Prerequisites
+
+- Azure OpenAI endpoint and deployment
+- A running Valkey server (any version):
+
+```bash
+docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+```
+
+## Environment Variables
+
+| Variable | Description | Default |
+|---|---|---|
+| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint URL | (required) |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Model deployment name | `gpt-5.4-mini` |
+| `VALKEY_CONNECTION` | Valkey connection string | `localhost:6379` |
+
+## Running
+
+```bash
+dotnet run
+```
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="AWSSDK.Extensions.Bedrock.MEAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Valkey\Microsoft.Agents.AI.Valkey.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,57 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates using Valkey for persistent chat history with the Agent Framework,
+// powered by Amazon Bedrock.
+//
+// Prerequisites:
+//   - A running Valkey server (any version):
+//       docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+//   - AWS credentials configured (environment variables, AWS profile, or IAM role)
+//   - Access to an Amazon Bedrock model (e.g., Anthropic Claude)
+
+using Amazon;
+using Amazon.BedrockRuntime;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Valkey;
+using Microsoft.Extensions.AI;
+using Valkey.Glide;
+
+var awsRegion = Environment.GetEnvironmentVariable("AWS_REGION") ?? "us-east-1";
+var modelId = Environment.GetEnvironmentVariable("BEDROCK_MODEL_ID") ?? "anthropic.claude-3-5-sonnet-20241022-v2:0";
+var valkeyConnection = Environment.GetEnvironmentVariable("VALKEY_CONNECTION") ?? "localhost:6379";
+
+// Create the Bedrock runtime client.
+var bedrockRuntime = new AmazonBedrockRuntimeClient(RegionEndpoint.GetBySystemName(awsRegion));
+IChatClient chatClient = bedrockRuntime.AsIChatClient(modelId);
+
+var connection = await ConnectionMultiplexer.ConnectAsync(valkeyConnection);
+
+Console.WriteLine("=== ValkeyChatHistoryProvider — Persistent Chat History (Bedrock) ===\n");
+
+var historyProvider = new ValkeyChatHistoryProvider(
+    connection,
+    _ => new ValkeyChatHistoryProvider.State($"bedrock-sample-{Guid.NewGuid():N}"),
+    new ValkeyChatHistoryProviderOptions
+    {
+        KeyPrefix = "bedrock_chat",
+        MaxMessages = 20
+    });
+
+AIAgent historyAgent = chatClient.AsAIAgent(new ChatClientAgentOptions()
+{
+    ChatOptions = new() { Instructions = "You are a helpful assistant that remembers our conversation." },
+    ChatHistoryProvider = historyProvider
+});
+
+AgentSession session1 = await historyAgent.CreateSessionAsync();
+Console.WriteLine(await historyAgent.RunAsync("Hello! My name is Alex and I'm a software engineer.", session1));
+Console.WriteLine(await historyAgent.RunAsync("I'm working on a project using Valkey for caching.", session1));
+Console.WriteLine(await historyAgent.RunAsync("What do you remember about me?", session1));
+
+var messageCount = await historyProvider.GetMessageCountAsync(session1);
+Console.WriteLine($"\n  Stored {messageCount} messages in Valkey.\n");
+
+// Clean up
+connection.Dispose();
+
+Console.WriteLine("Done!");
@@ -0,0 +1,41 @@
+# Agent with Memory Using Valkey + Amazon Bedrock
+
+This sample demonstrates using Valkey for persistent chat history with the Agent Framework, powered by Amazon Bedrock via the `AWSSDK.Extensions.Bedrock.MEAI` adapter.
+
+## Components
+
+- **ValkeyChatHistoryProvider** — Persists conversation history across sessions using Valkey lists. Works with any Valkey or Redis OSS server (no search module required).
+- **Amazon Bedrock** — Provides the LLM via `AWSSDK.Extensions.Bedrock.MEAI`, which implements `IChatClient` from `Microsoft.Extensions.AI`.
+
+## Prerequisites
+
+- AWS credentials configured (environment variables, AWS CLI profile, or IAM role)
+- Access to an Amazon Bedrock model (e.g., Anthropic Claude 3.5 Sonnet)
+- A running Valkey server (any version):
+
+```bash
+docker run -d --name valkey -p 6379:6379 valkey/valkey:latest
+```
+
+## Environment Variables
+
+| Variable | Description | Default |
+|---|---|---|
+| `AWS_REGION` | AWS region for Bedrock | `us-east-1` |
+| `BEDROCK_MODEL_ID` | Bedrock model identifier | `anthropic.claude-3-5-sonnet-20241022-v2:0` |
+| `VALKEY_CONNECTION` | Valkey connection string | `localhost:6379` |
+| `AWS_ACCESS_KEY_ID` | AWS access key (if not using profile/role) | — |
+| `AWS_SECRET_ACCESS_KEY` | AWS secret key (if not using profile/role) | — |
+
+## Running
+
+```bash
+# Using default AWS credential chain (profile, env vars, or IAM role)
+dotnet run
+
+# Or with explicit credentials
+export AWS_ACCESS_KEY_ID="your-access-key"
+export AWS_SECRET_ACCESS_KEY="your-secret-key"
+export AWS_REGION="us-east-1"
+dotnet run
+```
@@ -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.
@@ -23,7 +23,7 @@
    <PackageReference Include="Azure.AI.OpenAI" Version="2.9.0-beta.1" />
    <PackageReference Include="Azure.Identity" Version="1.19.0" />
    <PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-rc4" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.4.0" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.6.0" />
    <PackageReference Include="Neo4j.AgentFramework.GraphRAG" Version="0.1.0-preview.2" />
    <PackageReference Include="Neo4j.Driver" Version="5.28.0" />
  </ItemGroup>
@@ -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
--- a/Show More
+++ b/Show More