Python: Bump Python version to 1.0.1 for a release (#5196)

* Bump Python version to 1.1.0 for a release

* Fix changelog

* 1.0.1 instead of 1.1.0

* Update CHANGELOG.md

* update version and changelog

* Bump lower bounds

.github/workflows/dotnet-verify-samples.yml

@@ -0,0 +1,136 @@
+#
+# Runs the .NET sample verification tool, which builds and executes sample projects
+# and verifies their output using deterministic checks and AI-powered verification.
+#
+# Results are displayed as a GitHub Job Summary and the CSV report is uploaded as an artifact.
+#
+
+name: dotnet-verify-samples
+
+on:
+  workflow_dispatch:
+    inputs:
+      category:
+        description: "Sample category to run (blank for all)"
+        required: false
+        type: choice
+        options:
+          - ""
+          - "01-get-started"
+          - "02-agents"
+          - "03-workflows"
+      parallelism:
+        description: "Max parallel sample runs"
+        required: false
+        default: "8"
+        type: string
+  schedule:
+    - cron: "0 6 * * 1-5" # Weekdays at 6:00 UTC
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+  id-token: write
+
+jobs:
+  verify-samples:
+    runs-on: ubuntu-latest
+    environment: 'integration'
+    timeout-minutes: 90
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          persist-credentials: false
+          sparse-checkout: |
+            .
+            .github
+            dotnet
+            workflow-samples
+
+      - name: Setup dotnet
+        uses: actions/setup-dotnet@v5.2.0
+        with:
+          global-json-file: ${{ github.workspace }}/dotnet/global.json
+
+      - name: Azure CLI Login
+        if: github.event_name != 'pull_request'
+        uses: azure/login@v2
+        with:
+          client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+
+      - name: Generate filtered solution
+        shell: pwsh
+        run: |
+          ./dotnet/eng/scripts/New-FilteredSolution.ps1 `
+            -Solution dotnet/agent-framework-dotnet.slnx `
+            -TargetFramework net10.0 `
+            -Configuration Debug `
+            -OutputPath dotnet/filtered.slnx `
+            -Verbose
+
+      - name: Build solution
+        shell: bash
+        run: dotnet build dotnet/filtered.slnx -f net10.0 --warnaserror
+
+      - name: Run verify-samples
+        id: verify
+        working-directory: dotnet
+        shell: bash
+        run: |
+          CATEGORY_ARG=""
+          if [ -n "$CATEGORY_INPUT" ]; then
+            CATEGORY_ARG="--category $CATEGORY_INPUT"
+          fi
+
+          dotnet run --project eng/verify-samples -- \
+            $CATEGORY_ARG \
+            --parallel "$PARALLELISM" \
+            --md results.md \
+            --csv results.csv \
+            --log results.log
+        env:
+          CATEGORY_INPUT: ${{ github.event.inputs.category || '' }}
+          PARALLELISM: ${{ github.event.inputs.parallelism || '8' }}
+          # OpenAI Models
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          OPENAI_CHAT_MODEL_NAME: ${{ vars.OPENAI_CHAT_MODEL_NAME }}
+          OPENAI_REASONING_MODEL_NAME: ${{ vars.OPENAI_REASONING_MODEL_NAME }}
+          # Azure OpenAI Models
+          AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_ENDPOINT: ${{ vars.AZURE_OPENAI_ENDPOINT }}
+          # Azure AI Foundry
+          AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+          AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZURE_AI_MODEL_DEPLOYMENT_NAME }}
+          AZURE_AI_BING_CONNECTION_ID: ${{ vars.AZURE_AI_BING_CONNECTION_ID }}
+
+      - name: Write Job Summary
+        if: always()
+        working-directory: dotnet
+        shell: bash
+        run: |
+          if [ -f results.md ]; then
+            cat results.md >> "$GITHUB_STEP_SUMMARY"
+          else
+            echo "⚠️ No results.md generated — verify-samples may have failed to start." >> "$GITHUB_STEP_SUMMARY"
+          fi
+
+      - name: Upload results
+        if: always()
+        uses: actions/upload-artifact@v7
+        with:
+          name: verify-samples-results
+          path: |
+            dotnet/results.csv
+            dotnet/results.log
+          if-no-files-found: warn
+
+      - name: Fail if samples failed
+        if: always() && steps.verify.outcome == 'failure'
+        shell: bash
+        run: exit 1

.github/workflows/python-merge-tests.yml

@@ -115,12 +115,13 @@ jobs:
           -m "not integration"
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
+          --junitxml=pytest.xml
         working-directory: ./python
       - name: Surface failing tests
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false
@@ -163,6 +164,7 @@ jobs:
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
+          --junitxml=pytest.xml
         working-directory: ./python
       - name: Test OpenAI samples
         timeout-minutes: 10
@@ -173,7 +175,7 @@ jobs:
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false
@@ -225,6 +227,7 @@ jobs:
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
+          --junitxml=pytest.xml
         working-directory: ./python
       - name: Test Azure samples
         timeout-minutes: 10
@@ -235,7 +238,7 @@ jobs:
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false
@@ -285,6 +288,7 @@ jobs:
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
+          --junitxml=pytest.xml
         working-directory: ./python
       - name: Stop local MCP server
         if: always()
@@ -310,7 +314,7 @@ jobs:
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false
@@ -375,12 +379,13 @@ jobs:
           -x
           --timeout=360 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
+          --junitxml=pytest.xml
         working-directory: ./python
       - name: Surface failing tests
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false
@@ -430,12 +435,13 @@ jobs:
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
+          --junitxml=pytest.xml
         working-directory: ./python
       - name: Surface failing tests
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false
@@ -489,13 +495,13 @@ jobs:
           echo "Cosmos DB emulator did not become ready in time." >&2
           exit 1
       - name: Test with pytest (Cosmos integration)
-        run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
+        run: uv run --directory packages/azure-cosmos poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5 --junitxml=pytest.xml
         working-directory: ./python
       - name: Surface failing tests
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false

.github/workflows/python-tests.yml

@@ -40,7 +40,7 @@ jobs:
           UV_CACHE_DIR: /tmp/.uv-cache
       # Unit tests
       - name: Run all tests
-        run: uv run poe test -A
+        run: uv run poe test -A --junitxml=pytest.xml
         working-directory: ./python
 
       # Surface failing tests
@@ -48,7 +48,7 @@ jobs:
         if: always()
         uses: pmeier/pytest-results-action@v0.7.2
         with:
-          path: ./python/**.xml
+          path: ./python/pytest.xml
           summary: true
           display-options: fEX
           fail-on-empty: false

.gitignore

@@ -47,6 +47,8 @@ htmlcov/
 .cache
 nosetests.xml
 coverage.xml
+pytest.xml
+python-coverage.xml
 *.cover
 *.py,cover
 .hypothesis/
@@ -230,3 +232,6 @@ local.settings.json
 # Database files
 *.db
 python/dotnet-ref
+
+# Generated filtered solution files (created by eng/scripts/New-FilteredSolution.ps1)
+dotnet/filtered-*.slnx

README.md

@@ -28,7 +28,7 @@ Welcome to Microsoft's comprehensive multi-language framework for building, orch
 Python
 
 ```bash
-pip install agent-framework --pre
+pip install agent-framework
 # This will install all sub-packages, see `python/packages` for individual packages.
 # It may take a minute on first install on Windows.
 ```
@@ -90,7 +90,7 @@ Still have questions? Join our [weekly office hours](./COMMUNITY.md#public-commu
 Create a simple Azure Responses Agent that writes a haiku about the Microsoft Agent Framework
 
 ```python
-# pip install agent-framework --pre
+# pip install agent-framework
 # Use `az login` to authenticate with Azure CLI
 import os
 import asyncio
@@ -120,38 +120,38 @@ if __name__ == "__main__":
 ```
 
 ### Basic Agent - .NET
+Create a simple Agent, using Microsoft Foundry with token-based auth, that writes a haiku about the Microsoft Agent Framework
+
+```c#
+// dotnet add package Microsoft.Agents.AI.Foundry
+// Use `az login` to authenticate with Azure CLI
+using Azure.AI.Projects;
+using Azure.Identity;
+using System;
+using Azure.AI.Projects;
+using Azure.Identity;
+
+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 agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+
+Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
+```
 
 Create a simple Agent, using OpenAI Responses, that writes a haiku about the Microsoft Agent Framework
 
 ```c#
-// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
-using Microsoft.Agents.AI;
+// dotnet add package Microsoft.Agents.AI.OpenAI
+using System;
 using OpenAI;
 using OpenAI.Responses;
 
 // Replace the <apikey> with your OpenAI API key.
 var agent = new OpenAIClient("<apikey>")
-    .GetResponsesClient("gpt-4o-mini")
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
-
-Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
-```
-
-Create a simple Agent, using Microsoft Foundry with token-based auth, that writes a haiku about the Microsoft Agent Framework
-
-```c#
-// dotnet add package Microsoft.Agents.AI.AzureAI --prerelease
-// dotnet add package Azure.Identity
-// Use `az login` to authenticate with Azure CLI
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
-    .AsAIAgent(model: deploymentName, name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+    .GetResponsesClient()
+    .AsAIAgent(model: "gpt-5.4-mini", name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
 
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -207,4 +207,9 @@ The samples typically read configuration from environment variables. Common requ
 
 ## Important Notes
 
-If you use the Microsoft Agent Framework to build applications that operate with third-party servers or agents, you do so at your own risk. We recommend reviewing all data being shared with third-party servers or agents and being cognizant of third-party practices for retention and location of data. It is your responsibility to manage whether your data will flow outside of your organization's Azure compliance and geographic boundaries and any related implications.
+> [!IMPORTANT]
+> If you use Microsoft Agent Framework to build applications that operate with any third-party servers, agents, code, or non-Azure Direct models (“Third-Party Systems”), you do so at your own risk. Third-Party Systems are Non-Microsoft Products under the Microsoft Product Terms and are governed by their own third-party license terms. You are responsible for any usage and associated costs.
+>
+>We recommend reviewing all data being shared with and received from Third-Party Systems and being cognizant of third-party practices for handling, sharing, retention and location of data. It is your responsibility to manage whether your data will flow outside of your organization’s Azure compliance and geographic boundaries and any related implications, and that appropriate permissions, boundaries and approvals are provisioned.
+> 
+>You are responsible for carefully reviewing and testing applications you build using Microsoft Agent Framework in the context of your specific use cases, and making all appropriate decisions and customizations. This includes implementing your own responsible AI mitigations such as metaprompt, content filters, or other safety systems, and ensuring your applications meet appropriate quality, reliability, security, and trustworthiness standards. See also: [Transparency FAQ](./TRANSPARENCY_FAQ.md)

dotnet/.github/skills/verify-samples-tool/SKILL.md

@@ -9,9 +9,16 @@ The `verify-samples` project (`dotnet/eng/verify-samples/`) is an automated tool
 
 ## Running verify-samples
 
+**Important:** By default, samples must be pre-built before running verify-samples. Build the solution first, or pass `--build` to build samples during the run:
+
 ```bash
 cd dotnet
+dotnet build agent-framework-dotnet.slnx -f net10.0
+```
 
+Then run verify-samples:
+
+```bash
 # Run all samples across all categories
 dotnet run --project eng/verify-samples -- --log results.log --csv results.csv
 
@@ -24,8 +31,12 @@ dotnet run --project eng/verify-samples -- Agent_Step02_StructuredOutput Agent_S
 # Control parallelism (default 8)
 dotnet run --project eng/verify-samples -- --parallel 8 --log results.log
 
+# Build samples during run (skips the need for a prior build step)
+# This may cause build conflicts as multiple samples are built in parallel, so use with caution
+dotnet run --project eng/verify-samples -- --build --log results.log
+
 # Combine options
-dotnet run --project eng/verify-samples -- --category 03-workflows --parallel 4 --log results.log --csv results.csv
+dotnet run --project eng/verify-samples -- --category 03-workflows --parallel 4 --log results.log --csv results.csv --md results.md
 ```
 
 ### Required Environment Variables
@@ -40,6 +51,7 @@ Individual samples require their own env vars (e.g., `AZURE_AI_PROJECT_ENDPOINT`
 
 - `--log results.log` — detailed per-sample log with stdout/stderr, AI reasoning, and a summary
 - `--csv results.csv` — tabular summary with Sample, ProjectPath, Status, FailedChecks, and Failures columns
+- `--md results.md` — Markdown summary with results table and collapsible failure details (suitable for GitHub PR comments)
 
 ## Sample Categories
 

dotnet/AGENTS.md

@@ -36,7 +36,7 @@ using types like `IChatClient`, `FunctionInvokingChatClient`, `AITool`, `AIFunct
 - **Async**: Use `Async` suffix for methods returning `Task`/`ValueTask`
 - **Private classes**: Should be `sealed` unless subclassed
 - **Config**: Read from environment variables with `UPPER_SNAKE_CASE` naming
-- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking
+- **Tests**: Add Arrange/Act/Assert comments; use Moq for mocking; test methods returning `Task`/`ValueTask` must use the `Async` suffix.
 
 ## Key Design Principles
 

dotnet/Directory.Packages.props

@@ -11,7 +11,7 @@
   </PropertyGroup>
   <ItemGroup>
     <!-- Aspire.* -->
-    <PackageVersion Include="Anthropic" Version="12.8.0" />
+    <PackageVersion Include="Anthropic" Version="12.11.0" />
     <PackageVersion Include="Anthropic.Foundry" Version="0.4.2" />
     <PackageVersion Include="Aspire.Azure.AI.OpenAI" Version="13.0.0-preview.1.25560.3" />
     <PackageVersion Include="Aspire.Hosting.AppHost" Version="$(AspireAppHostSdkVersion)" />

dotnet/agent-framework-dotnet.slnx

@@ -106,6 +106,9 @@
     <File Path="samples/02-agents/AgentSkills/README.md" />
     <Project Path="samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj" />
     <Project Path="samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step03_ClassBasedSkills/Agent_Step03_ClassBasedSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step04_MixedSkills/Agent_Step04_MixedSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step05_SkillsWithDI/Agent_Step05_SkillsWithDI.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
     <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />

dotnet/eng/verify-samples/AgentsSamples.cs

@@ -246,7 +246,7 @@ internal static class AgentsSamples
             ExpectedOutputDescription =
             [
                 "The output should contain information about both the current time and the weather in Seattle.",
-                "The weather information should reference the plugin result: cloudy with a high of 15°C.",
+                "The weather information should be similar to: cloudy with a high of 15°C. Exact phrasing may vary.",
                 "The output should not contain error messages or stack traces.",
             ],
         },
@@ -521,7 +521,7 @@ internal static class AgentsSamples
             OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
             ExpectedOutputDescription =
             [
-                "The output should demonstrate server-side conversation sessions with non-streaming and streaming turns.",
+                "The output should contain multiple joke responses showing a multi-turn conversation.",
                 "The output should not contain error messages or stack traces.",
             ],
         },

dotnet/eng/verify-samples/MarkdownResultWriter.cs

@@ -0,0 +1,98 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text;
+
+namespace VerifySamples;
+
+/// <summary>
+/// Writes a Markdown summary of sample verification results.
+/// </summary>
+internal static class MarkdownResultWriter
+{
+    /// <summary>
+    /// Writes the results to a Markdown file at the specified path.
+    /// </summary>
+    public static async Task WriteAsync(
+        string path,
+        IReadOnlyList<VerificationResult> orderedResults,
+        IReadOnlyList<(string Name, string Reason)> skipped,
+        TimeSpan elapsed)
+    {
+        var passCount = orderedResults.Count(r => r.Passed);
+        var failCount = orderedResults.Count(r => !r.Passed);
+
+        var sb = new StringBuilder();
+        sb.AppendLine("# Sample Verification Results");
+        sb.AppendLine();
+        sb.AppendLine($"**{passCount} passed, {failCount} failed, {skipped.Count} skipped** | Elapsed: {elapsed.Hours:D2}:{elapsed.Minutes:D2}:{elapsed.Seconds:D2}");
+        sb.AppendLine();
+
+        // Results table
+        sb.AppendLine("| Sample | Status | Failed Checks | Failures |");
+        sb.AppendLine("|--------|--------|---------------|----------|");
+
+        foreach (var result in orderedResults)
+        {
+            var status = result.Passed ? "✅ PASSED" : "❌ FAILED";
+            var failedChecks = result.Failures.Count;
+            var failures = MdEscape(string.Join("; ", result.Failures));
+            sb.AppendLine($"| {MdEscape(result.SampleName)} | {status} | {failedChecks} | {failures} |");
+        }
+
+        foreach (var (name, reason) in skipped)
+        {
+            sb.AppendLine($"| {MdEscape(name)} | ⏭️ SKIPPED | 0 | {MdEscape(reason)} |");
+        }
+
+        // Collapsible AI reasoning details for failures
+        var failures2 = orderedResults.Where(r => !r.Passed && !string.IsNullOrEmpty(r.AIReasoning)).ToList();
+        if (failures2.Count > 0)
+        {
+            sb.AppendLine();
+            sb.AppendLine("## Failure Details");
+            sb.AppendLine();
+
+            foreach (var result in failures2)
+            {
+                sb.AppendLine($"<details><summary><strong>{HtmlEscape(result.SampleName)}</strong></summary>");
+                sb.AppendLine();
+                if (result.Failures.Count > 0)
+                {
+                    foreach (var failure in result.Failures)
+                    {
+                        sb.AppendLine($"- {MdEscape(failure)}");
+                    }
+
+                    sb.AppendLine();
+                }
+
+                sb.AppendLine("**AI Reasoning:**");
+                sb.AppendLine();
+                sb.AppendLine("```");
+                sb.AppendLine(result.AIReasoning);
+                sb.AppendLine("```");
+                sb.AppendLine();
+                sb.AppendLine("</details>");
+                sb.AppendLine();
+            }
+        }
+
+        await File.WriteAllTextAsync(path, sb.ToString());
+    }
+
+    /// <summary>
+    /// Escapes pipe characters and newlines for use inside Markdown table cells.
+    /// </summary>
+    private static string MdEscape(string value)
+    {
+        return value.Replace("|", "\\|").Replace("\n", " ").Replace("\r", "");
+    }
+
+    /// <summary>
+    /// Escapes HTML special characters for use inside HTML tags.
+    /// </summary>
+    private static string HtmlEscape(string value)
+    {
+        return value.Replace("&", "&amp;").Replace("<", "&lt;").Replace(">", "&gt;").Replace("\"", "&quot;");
+    }
+}

dotnet/eng/verify-samples/Program.cs

@@ -13,6 +13,10 @@
 //   dotnet run -- --parallel 16                         # Run up to 16 samples concurrently
 //   dotnet run -- --log results.log                     # Write sequential log to file
 //   dotnet run -- --csv results.csv                     # Write CSV summary to file
+//   dotnet run -- --md results.md                       # Write Markdown summary to file
+//   dotnet run -- --build                                # Build samples during run (default: --no-build)
+// Note: By default, this tool expects sample build outputs to already exist.
+// Pre-build the solution before running, or pass --build to avoid missing build output failures.
 //
 // Required environment variables (for AI-powered samples):
 //   AZURE_OPENAI_ENDPOINT
@@ -62,7 +66,7 @@ try
     // Run all samples
     var reporter = new ConsoleReporter();
     var verifier = new SampleVerifier(chatClient);
-    var orchestrator = new VerificationOrchestrator(verifier, reporter, dotnetRoot, TimeSpan.FromMinutes(3), logWriter);
+    var orchestrator = new VerificationOrchestrator(verifier, reporter, dotnetRoot, TimeSpan.FromMinutes(3), logWriter, buildSamples: options.BuildSamples);
 
     var run = await orchestrator.RunAllAsync(options.Samples, options.MaxParallelism);
 
@@ -90,6 +94,13 @@ try
         Console.WriteLine($"CSV written to: {options.CsvFilePath}");
     }
 
+    // Write Markdown summary
+    if (options.MarkdownFilePath is not null)
+    {
+        await MarkdownResultWriter.WriteAsync(options.MarkdownFilePath, orderedResults, run.Skipped, stopwatch.Elapsed);
+        Console.WriteLine($"Markdown written to: {options.MarkdownFilePath}");
+    }
+
     return orderedResults.Any(r => !r.Passed) ? 1 : 0;
 }
 finally

dotnet/eng/verify-samples/SampleRunner.cs

@@ -20,23 +20,32 @@ internal static class SampleRunner
 {
     /// <summary>
     /// Runs <c>dotnet run --framework net10.0</c> in the given project directory.
+    /// When <paramref name="build"/> is false (the default), <c>--no-build</c> is passed
+    /// to skip building, assuming the project was pre-built.
     /// </summary>
     public static Task<SampleRunResult> RunAsync(
         string projectPath,
         TimeSpan timeout,
+        bool build = false,
         CancellationToken cancellationToken = default)
-        => RunAsync(projectPath, "run --framework net10.0", timeout, inputs: null, inputDelayMs: 0, cancellationToken: cancellationToken);
+        => RunAsync(projectPath, DotnetRunArgs(build), timeout, inputs: null, inputDelayMs: 0, cancellationToken: cancellationToken);
 
     /// <summary>
     /// Runs <c>dotnet run --framework net10.0</c> with stdin inputs.
+    /// When <paramref name="build"/> is false (the default), <c>--no-build</c> is passed
+    /// to skip building, assuming the project was pre-built.
     /// </summary>
     public static Task<SampleRunResult> RunAsync(
         string projectPath,
         TimeSpan timeout,
         string?[]? inputs,
         int inputDelayMs = 2000,
+        bool build = false,
         CancellationToken cancellationToken = default)
-        => RunAsync(projectPath, "run --framework net10.0", timeout, inputs, inputDelayMs, cancellationToken);
+        => RunAsync(projectPath, DotnetRunArgs(build), timeout, inputs, inputDelayMs, cancellationToken);
+
+    private static string DotnetRunArgs(bool build) =>
+        $"run {(build ? "" : "--no-build")} --framework net10.0";
 
     /// <summary>
     /// Runs an arbitrary <c>dotnet</c> command in the given working directory.

dotnet/eng/verify-samples/SampleVerifier.cs

@@ -1,5 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.
 
+using System.ComponentModel;
 using System.Text.Json.Serialization;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
@@ -27,11 +28,19 @@ internal sealed class SampleVerifier
                 instructions: """
                     You are a test output verifier. You will be given:
                     1. The actual stdout output of a program
-                    2. A list of expectations about what the output should contain or demonstrate
+                    2. The stderr output (if any)
+                    3. A list of expectations about what the output should contain or demonstrate
 
                     Your job is to determine whether the actual output satisfies each expectation.
                     Be reasonable — the output comes from an LLM so exact wording won't match, but the
                     semantic intent should be clearly satisfied.
+
+                    In your response, you MUST:
+                    - Always provide ai_reasoning with a brief overall assessment.
+                    - Always provide exactly one entry in expectation_results for each expectation,
+                      in the same order as the input list.
+                    - For each expectation_results entry, echo the expectation text in the expectation
+                      field and explain your assessment in the detail field, citing evidence from the output.
                     """,
                 name: "OutputVerifier");
         }
@@ -78,7 +87,7 @@ internal sealed class SampleVerifier
             }
             else
             {
-                var aiResult = await this.VerifyWithAIAsync(run.Stdout, sample.ExpectedOutputDescription);
+                var aiResult = await this.VerifyWithAIAsync(run.Stdout, run.Stderr, sample.ExpectedOutputDescription);
                 aiReasoning = aiResult.Reasoning;
 
                 foreach (var unmet in aiResult.UnmetExpectations)
@@ -100,16 +109,28 @@ internal sealed class SampleVerifier
     }
 
     private async Task<(string Reasoning, List<string> UnmetExpectations)> VerifyWithAIAsync(
-        string actualOutput,
+        string stdout,
+        string stderr,
         string[] expectations)
     {
         var expectationList = string.Join("\n", expectations.Select((e, i) => $"  {i + 1}. {e}"));
+
+        var stderrSection = string.IsNullOrWhiteSpace(stderr)
+            ? ""
+            : $"""
+
+                Stderr output:
+                ---
+                {Truncate(stderr, 2000)}
+                ---
+                """;
+
         var prompt = $"""
             Actual program output:
             ---
-            {Truncate(actualOutput, 4000)}
+            {Truncate(stdout, 4000)}
             ---
-
+            {stderrSection}
             Expectations to verify:
             {expectationList}
 
@@ -126,7 +147,9 @@ internal sealed class SampleVerifier
                 return ($"AI verification returned null result. Raw: {response.Text}", ["AI verification returned null result."]);
             }
 
-            var reasoning = result.Reasoning ?? "(no reasoning provided)";
+            var reasoning = string.IsNullOrWhiteSpace(result.AIReasoning)
+                ? "(no reasoning provided)"
+                : result.AIReasoning;
 
             // Collect unmet expectations as individual failures
             var unmet = new List<string>();
@@ -174,12 +197,14 @@ internal sealed class AIVerificationResponse
     public bool Pass { get; set; }
 
     /// <summary>Brief explanation of the overall assessment.</summary>
-    [JsonPropertyName("reasoning")]
-    public string? Reasoning { get; set; }
+    [JsonPropertyName("ai_reasoning")]
+    [Description("Always required. Brief explanation of the overall assessment, covering all expectations.")]
+    public string AIReasoning { get; set; } = string.Empty;
 
     /// <summary>Per-expectation results.</summary>
     [JsonPropertyName("expectation_results")]
-    public List<ExpectationResult>? ExpectationResults { get; set; }
+    [Description("Always required. One entry per expectation, in the same order as the input list.")]
+    public List<ExpectationResult> ExpectationResults { get; set; } = [];
 }
 
 /// <summary>
@@ -190,7 +215,8 @@ internal sealed class ExpectationResult
 {
     /// <summary>The expectation text that was evaluated.</summary>
     [JsonPropertyName("expectation")]
-    public string? Expectation { get; set; }
+    [Description("Echo back the expectation text being evaluated.")]
+    public string Expectation { get; set; } = string.Empty;
 
     /// <summary>Whether this expectation was met.</summary>
     [JsonPropertyName("met")]
@@ -198,5 +224,6 @@ internal sealed class ExpectationResult
 
     /// <summary>Detail about how the expectation was or was not met.</summary>
     [JsonPropertyName("detail")]
-    public string? Detail { get; set; }
+    [Description("Explain how the expectation was or was not met, citing specific evidence from the output.")]
+    public string Detail { get; set; } = string.Empty;
 }

dotnet/eng/verify-samples/VerificationOrchestrator.cs

@@ -14,19 +14,22 @@ internal sealed class VerificationOrchestrator
     private readonly LogFileWriter? _logWriter;
     private readonly string _dotnetRoot;
     private readonly TimeSpan _timeout;
+    private readonly bool _buildSamples;
 
     public VerificationOrchestrator(
         SampleVerifier verifier,
         ConsoleReporter reporter,
         string dotnetRoot,
         TimeSpan timeout,
-        LogFileWriter? logWriter = null)
+        LogFileWriter? logWriter = null,
+        bool buildSamples = false)
     {
         this._verifier = verifier;
         this._reporter = reporter;
         this._logWriter = logWriter;
         this._dotnetRoot = dotnetRoot;
         this._timeout = timeout;
+        this._buildSamples = buildSamples;
     }
 
     /// <summary>
@@ -136,8 +139,8 @@ internal sealed class VerificationOrchestrator
 
             var projectPath = Path.Combine(this._dotnetRoot, sample.ProjectPath);
             var run = sample.Inputs.Length > 0
-                ? await SampleRunner.RunAsync(projectPath, this._timeout, sample.Inputs, sample.InputDelayMs)
-                : await SampleRunner.RunAsync(projectPath, this._timeout);
+                ? await SampleRunner.RunAsync(projectPath, this._timeout, sample.Inputs, sample.InputDelayMs, build: this._buildSamples)
+                : await SampleRunner.RunAsync(projectPath, this._timeout, build: this._buildSamples);
 
             log.Add($"[{sample.Name}] Completed ({run.Elapsed.TotalSeconds:F1}s, exit={run.ExitCode})");
             this._reporter.WriteLineWithPrefix(

dotnet/eng/verify-samples/VerifyOptions.cs

@@ -17,11 +17,22 @@ internal sealed class VerifyOptions
     /// </summary>
     public string? CsvFilePath { get; init; }
 
+    /// <summary>
+    /// Path to write a Markdown summary file, or <c>null</c> to skip.
+    /// </summary>
+    public string? MarkdownFilePath { get; init; }
+
     /// <summary>
     /// Path to write a sequential log file, or <c>null</c> to skip.
     /// </summary>
     public string? LogFilePath { get; init; }
 
+    /// <summary>
+    /// When true, samples are built as part of <c>dotnet run</c>.
+    /// When false (the default), <c>--no-build</c> is passed, assuming a prior build step.
+    /// </summary>
+    public bool BuildSamples { get; init; }
+
     /// <summary>
     /// The filtered list of samples to process.
     /// </summary>
@@ -49,6 +60,8 @@ internal sealed class VerifyOptions
         var categoryFilter = ExtractArg(argList, "--category");
         var logFilePath = ExtractArg(argList, "--log");
         var csvFilePath = ExtractArg(argList, "--csv");
+        var markdownFilePath = ExtractArg(argList, "--md");
+        var buildSamples = ExtractFlag(argList, "--build");
 
         int maxParallelism = 8;
         var parallelArg = ExtractArg(argList, "--parallel");
@@ -98,6 +111,8 @@ internal sealed class VerifyOptions
             MaxParallelism = maxParallelism,
             LogFilePath = logFilePath,
             CsvFilePath = csvFilePath,
+            MarkdownFilePath = markdownFilePath,
+            BuildSamples = buildSamples,
             Samples = samples,
         };
     }
@@ -121,4 +136,16 @@ internal sealed class VerifyOptions
         list.RemoveRange(idx, 2);
         return value;
     }
+
+    private static bool ExtractFlag(List<string> list, string flag)
+    {
+        var idx = list.IndexOf(flag);
+        if (idx < 0)
+        {
+            return false;
+        }
+
+        list.RemoveAt(idx);
+        return true;
+    }
 }

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

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

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

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

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

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

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

@@ -16,7 +16,7 @@ using OpenAI.Chat;
 using SampleApp;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

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

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

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

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

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

@@ -97,7 +97,7 @@ Console.WriteLine("""
     """);
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT environment variable is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Log application startup
 appLogger.LogInformation("OpenTelemetry Aspire Demo application started");

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

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

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

@@ -9,7 +9,7 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 const string JokerName = "Joker";
 const string JokerInstructions = "You are good at telling jokes.";

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

@@ -22,5 +22,5 @@ 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-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

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

@@ -9,7 +9,7 @@ using Microsoft.Agents.AI;
 using Microsoft.Agents.AI.Foundry;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 const string JokerName = "JokerAgent";
 

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

@@ -22,5 +22,5 @@ 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-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -16,7 +16,7 @@ using OpenAI.Responses;
 
 // --- Configuration ---
 string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // --- Skills Provider ---
 // Discovers skills from the 'skills' directory containing SKILL.md files.

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

@@ -6,7 +6,7 @@ This sample demonstrates how to use **file-based Agent Skills** with a `ChatClie
 
 - Discovering skills from `SKILL.md` files on disk via `AgentFileSkillsSource`
 - The progressive disclosure pattern: advertise → load → read resources → run scripts
-- Using the `AgentSkillsProvider` constructor with a skill directory path and script executor
+- Using the `AgentSkillsProvider` constructor with a skill directory path and script runner
 - Running file-based scripts (Python) via a subprocess-based executor
 
 ## Skills Included
@@ -30,7 +30,7 @@ Converts between common units (miles↔km, pounds↔kg) using a multiplication f
 
 ```bash
 export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
 ```
 
 ### Run

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

@@ -16,7 +16,7 @@ using OpenAI.Responses;
 
 // --- Configuration ---
 string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // --- Build the code-defined skill ---
 var unitConverterSkill = new AgentInlineSkill(

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

@@ -31,7 +31,7 @@ Converts between common units using multiplication factors. Defined entirely in
 
 ```bash
 export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
 ```
 
 ### Run

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

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

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

@@ -0,0 +1,102 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to define Agent Skills as C# classes using AgentClassSkill.
+// Class-based skills bundle all components into a single class implementation.
+
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- Class-Based Skill ---
+// Instantiate the skill class.
+var unitConverter = new UnitConverterSkill();
+
+// --- Skills Provider ---
+var skillsProvider = new AgentSkillsProvider(unitConverter);
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Unit conversion ---
+Console.WriteLine("Converting units with class-based skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");
+
+/// <summary>
+/// A unit-converter skill defined as a C# class.
+/// </summary>
+/// <remarks>
+/// Class-based skills bundle all components (name, description, body, resources, scripts)
+/// into a single class.
+/// </remarks>
+internal sealed class UnitConverterSkill : AgentClassSkill
+{
+    private IReadOnlyList<AgentSkillResource>? _resources;
+    private IReadOnlyList<AgentSkillScript>? _scripts;
+
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; } = new(
+        "unit-converter",
+        "Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.");
+
+    /// <inheritdoc/>
+    protected override string Instructions => """
+        Use this skill when the user asks to convert between units.
+
+        1. Review the conversion-table resource to find the factor for the requested conversion.
+        2. Use the convert script, passing the value and factor from the table.
+        3. Present the result clearly with both units.
+        """;
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillResource>? Resources => this._resources ??=
+    [
+        CreateResource(
+            "conversion-table",
+            """
+            # Conversion Tables
+
+            Formula: **result = value × factor**
+
+            | From        | To          | Factor   |
+            |-------------|-------------|----------|
+            | miles       | kilometers  | 1.60934  |
+            | kilometers  | miles       | 0.621371 |
+            | pounds      | kilograms   | 0.453592 |
+            | kilograms   | pounds      | 2.20462  |
+            """),
+    ];
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillScript>? Scripts => this._scripts ??=
+    [
+        CreateScript("convert", ConvertUnits),
+    ];
+
+    private static string ConvertUnits(double value, double factor)
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    }
+}

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

@@ -0,0 +1,49 @@
+# Class-Based Agent Skills Sample
+
+This sample demonstrates how to define **Agent Skills as C# classes** using `AgentClassSkill`.
+
+## What it demonstrates
+
+- Creating skills as classes that extend `AgentClassSkill`
+- Bundling name, description, body, resources, and scripts into a single class
+- Using the `AgentSkillsProvider` constructor with class-based skills
+
+## Skills Included
+
+### unit-converter (class-based)
+
+A `UnitConverterSkill` class that converts between common units. Defined in `Program.cs`:
+
+- `conversion-table` — Static resource with factor table
+- `convert` — Script that performs `value × factor` conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with class-based skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```

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

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

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

@@ -0,0 +1,149 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates an advanced scenario: combining multiple skill types in a single agent
+// using AgentSkillsProviderBuilder. The builder is designed for cases where the simple
+// AgentSkillsProvider constructors are insufficient — for example, when you need to mix skill
+// sources, apply filtering, or configure cross-cutting options in one place.
+//
+// Three different skill sources are registered here:
+// 1. File-based: unit-converter (miles↔km, pounds↔kg) from SKILL.md on disk
+// 2. Code-defined: volume-converter (gallons↔liters) using AgentInlineSkill
+// 3. Class-based: temperature-converter (°F↔°C↔K) using AgentClassSkill
+//
+// For simpler, single-source scenarios, see the earlier steps in this sample series
+// (e.g., Step01 for file-based, Step02 for code-defined, Step03 for class-based).
+
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- 1. Code-Defined Skill: volume-converter ---
+var volumeConverterSkill = new AgentInlineSkill(
+    name: "volume-converter",
+    description: "Convert between gallons and liters using a multiplication factor.",
+    instructions: """
+        Use this skill when the user asks to convert between gallons and liters.
+
+        1. Review the volume-conversion-table resource to find the correct factor.
+        2. Use the convert-volume script, passing the value and factor.
+        """)
+    .AddResource("volume-conversion-table",
+        """
+        # Volume Conversion Table
+
+        Formula: **result = value × factor**
+
+        | From    | To      | Factor  |
+        |---------|---------|---------|
+        | gallons | liters  | 3.78541 |
+        | liters  | gallons | 0.264172|
+        """)
+    .AddScript("convert-volume", (double value, double factor) =>
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    });
+
+// --- 2. Class-Based Skill: temperature-converter ---
+var temperatureConverter = new TemperatureConverterSkill();
+
+// --- 3. Build provider combining all three source types ---
+var skillsProvider = new AgentSkillsProviderBuilder()
+    .UseFileSkill(Path.Combine(AppContext.BaseDirectory, "skills"))    // File-based: unit-converter
+    .UseSkill(volumeConverterSkill)                                    // Code-defined: volume-converter
+    .UseSkill(temperatureConverter)                                     // Class-based: temperature-converter
+    .UseFileScriptRunner(SubprocessScriptRunner.RunAsync)
+    .Build();
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "MultiConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units, volumes, and temperatures.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Use all three skills ---
+Console.WriteLine("Converting with mixed skills (file + code + class)");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "I need three conversions: " +
+    "1) How many kilometers is a marathon (26.2 miles)? " +
+    "2) How many liters is a 5-gallon bucket? " +
+    "3) What is 98.6°F in Celsius?");
+
+Console.WriteLine($"Agent: {response.Text}");
+
+/// <summary>
+/// A temperature-converter skill defined as a C# class.
+/// </summary>
+internal sealed class TemperatureConverterSkill : AgentClassSkill
+{
+    private IReadOnlyList<AgentSkillResource>? _resources;
+    private IReadOnlyList<AgentSkillScript>? _scripts;
+
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; } = new(
+        "temperature-converter",
+        "Convert between temperature scales (Fahrenheit, Celsius, Kelvin).");
+
+    /// <inheritdoc/>
+    protected override string Instructions => """
+        Use this skill when the user asks to convert temperatures.
+
+        1. Review the temperature-conversion-formulas resource for the correct formula.
+        2. Use the convert-temperature script, passing the value, source scale, and target scale.
+        3. Present the result clearly with both temperature scales.
+        """;
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillResource>? Resources => this._resources ??=
+    [
+        CreateResource(
+            "temperature-conversion-formulas",
+            """
+            # Temperature Conversion Formulas
+
+            | From        | To          | Formula                   |
+            |-------------|-------------|---------------------------|
+            | Fahrenheit  | Celsius     | °C = (°F − 32) × 5/9     |
+            | Celsius     | Fahrenheit  | °F = (°C × 9/5) + 32     |
+            | Celsius     | Kelvin      | K = °C + 273.15           |
+            | Kelvin      | Celsius     | °C = K − 273.15           |
+            """),
+    ];
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillScript>? Scripts => this._scripts ??=
+    [
+        CreateScript("convert-temperature", ConvertTemperature),
+    ];
+
+    private static string ConvertTemperature(double value, string from, string to)
+    {
+        double result = (from.ToUpperInvariant(), to.ToUpperInvariant()) switch
+        {
+            ("FAHRENHEIT", "CELSIUS") => Math.Round((value - 32) * 5.0 / 9.0, 2),
+            ("CELSIUS", "FAHRENHEIT") => Math.Round(value * 9.0 / 5.0 + 32, 2),
+            ("CELSIUS", "KELVIN") => Math.Round(value + 273.15, 2),
+            ("KELVIN", "CELSIUS") => Math.Round(value - 273.15, 2),
+            _ => throw new ArgumentException($"Unsupported conversion: {from} → {to}")
+        };
+
+        return JsonSerializer.Serialize(new { value, from, to, result });
+    }
+}

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

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,208 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use Dependency Injection (DI) with Agent Skills.
+// It shows two approaches side-by-side, each handling a different conversion domain:
+//
+// 1. Code-defined skill (AgentInlineSkill) — converts distances (miles ↔ kilometers).
+//    Resources and scripts are inline delegates that resolve services from IServiceProvider.
+//
+// 2. Class-based skill (AgentClassSkill) — converts weights (pounds ↔ kilograms).
+//    Resources and scripts are encapsulated in a class, also resolving services from IServiceProvider.
+//
+// Both skills share the same ConversionService registered in the DI container,
+// showing that DI works identically regardless of how the skill is defined.
+// When prompted with a question spanning both domains, the agent uses both skills.
+
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// --- DI Container ---
+// Register application services that skill resources and scripts can resolve at execution time.
+ServiceCollection services = new();
+services.AddSingleton<ConversionService>();
+
+IServiceProvider serviceProvider = services.BuildServiceProvider();
+
+// =====================================================================
+// Approach 1: Code-Defined Skill with DI (AgentInlineSkill)
+// =====================================================================
+// Handles distance conversions (miles ↔ kilometers).
+// Resources and scripts are inline delegates. Each delegate can declare
+// an IServiceProvider parameter that the framework injects automatically.
+
+var distanceSkill = new AgentInlineSkill(
+    name: "distance-converter",
+    description: "Convert between distance units. Use when asked to convert miles to kilometers or kilometers to miles.",
+    instructions: """
+        Use this skill when the user asks to convert between distance units (miles and kilometers).
+
+        1. Review the distance-table resource to find the factor for the requested conversion.
+        2. Use the convert script, passing the value and factor from the table.
+        """)
+    .AddResource("distance-table", (IServiceProvider serviceProvider) =>
+    {
+        var service = serviceProvider.GetRequiredService<ConversionService>();
+        return service.GetDistanceTable();
+    })
+    .AddScript("convert", (double value, double factor, IServiceProvider serviceProvider) =>
+    {
+        var service = serviceProvider.GetRequiredService<ConversionService>();
+        return service.Convert(value, factor);
+    });
+
+// =====================================================================
+// Approach 2: Class-Based Skill with DI (AgentClassSkill)
+// =====================================================================
+// Handles weight conversions (pounds ↔ kilograms).
+// Resources and scripts are encapsulated in a class. Factory methods
+// CreateResource and CreateScript accept delegates with IServiceProvider.
+//
+// Alternatively, class-based skills can accept dependencies through their
+// constructor. Register the skill class itself in the ServiceCollection and
+// resolve it from the container:
+//
+//   services.AddSingleton<WeightConverterSkill>();
+//   var weightSkill = serviceProvider.GetRequiredService<WeightConverterSkill>();
+
+var weightSkill = new WeightConverterSkill();
+
+// --- Skills Provider ---
+// Both skills are registered with the same provider so the agent can use either one.
+var skillsProvider = new AgentSkillsProvider(distanceSkill, weightSkill);
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(
+        options: new ChatClientAgentOptions
+        {
+            Name = "UnitConverterAgent",
+            ChatOptions = new()
+            {
+                Instructions = "You are a helpful assistant that can convert units.",
+            },
+            AIContextProviders = [skillsProvider],
+        },
+        model: deploymentName,
+        services: serviceProvider);
+
+// --- Example: Unit conversion ---
+// This prompt spans both domains, so the agent will use both skills.
+Console.WriteLine("Converting units with DI-powered skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");
+
+// ---------------------------------------------------------------------------
+// Class-Based Skill
+// ---------------------------------------------------------------------------
+
+/// <summary>
+/// A weight-converter skill defined as a C# class that uses Dependency Injection.
+/// </summary>
+/// <remarks>
+/// This skill resolves <see cref="ConversionService"/> from the DI container
+/// in both its resource and script functions. This enables clean separation of
+/// concerns and testability while retaining the class-based skill pattern.
+/// </remarks>
+internal sealed class WeightConverterSkill : AgentClassSkill
+{
+    private IReadOnlyList<AgentSkillResource>? _resources;
+    private IReadOnlyList<AgentSkillScript>? _scripts;
+
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; } = new(
+        "weight-converter",
+        "Convert between weight units. Use when asked to convert pounds to kilograms or kilograms to pounds.");
+
+    /// <inheritdoc/>
+    protected override string Instructions => """
+        Use this skill when the user asks to convert between weight units (pounds and kilograms).
+
+        1. Review the weight-table resource to find the factor for the requested conversion.
+        2. Use the convert script, passing the value and factor from the table.
+        3. Present the result clearly with both units.
+        """;
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillResource>? Resources => this._resources ??=
+    [
+        CreateResource("weight-table", (IServiceProvider serviceProvider) =>
+        {
+            var service = serviceProvider.GetRequiredService<ConversionService>();
+            return service.GetWeightTable();
+        }),
+    ];
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillScript>? Scripts => this._scripts ??=
+    [
+        CreateScript("convert", (double value, double factor, IServiceProvider serviceProvider) =>
+        {
+            var service = serviceProvider.GetRequiredService<ConversionService>();
+            return service.Convert(value, factor);
+        }),
+    ];
+}
+
+// ---------------------------------------------------------------------------
+// Services
+// ---------------------------------------------------------------------------
+
+/// <summary>
+/// Provides conversion rates between units.
+/// In a real application this could call an external API, read from a database,
+/// or apply time-varying exchange rates.
+/// </summary>
+internal sealed class ConversionService
+{
+    /// <summary>
+    /// Returns a markdown table of supported distance conversions.
+    /// </summary>
+    public string GetDistanceTable() =>
+        """
+        # Distance Conversions
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | miles       | kilometers  | 1.60934  |
+        | kilometers  | miles       | 0.621371 |
+        """;
+
+    /// <summary>
+    /// Returns a markdown table of supported weight conversions.
+    /// </summary>
+    public string GetWeightTable() =>
+        """
+        # Weight Conversions
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | pounds      | kilograms   | 0.453592 |
+        | kilograms   | pounds      | 2.20462  |
+        """;
+
+    /// <summary>
+    /// Converts a value by the given factor and returns a JSON result.
+    /// </summary>
+    public string Convert(double value, double factor)
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    }
+}

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

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

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

@@ -6,19 +6,32 @@ Samples demonstrating Agent Skills capabilities. Each sample shows a different w
 |--------|-------------|
 | [Agent_Step01_FileBasedSkills](Agent_Step01_FileBasedSkills/) | Define skills as `SKILL.md` files on disk with reference documents. Uses a unit-converter skill. |
 | [Agent_Step02_CodeDefinedSkills](Agent_Step02_CodeDefinedSkills/) | Define skills entirely in C# code using `AgentInlineSkill`, with static/dynamic resources and scripts. |
+| [Agent_Step03_ClassBasedSkills](Agent_Step03_ClassBasedSkills/) | Define skills as C# classes using `AgentClassSkill`. |
+| [Agent_Step04_MixedSkills](Agent_Step04_MixedSkills/) | **(Advanced)** Combine file-based, code-defined, and class-based skills using `AgentSkillsProviderBuilder`. |
+| [Agent_Step05_SkillsWithDI](Agent_Step05_SkillsWithDI/) | Use Dependency Injection with both code-defined (`AgentInlineSkill`) and class-based (`AgentClassSkill`) skills. |
 
 ## Key Concepts
 
-### File-Based vs Code-Defined Skills
+### Skill Types
 
-| Aspect | File-Based | Code-Defined |
-|--------|-----------|--------------|
-| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# |
-| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) |
-| Scripts | Supported via script executor delegate | `AddScript` delegates |
-| Discovery | Automatic from directory path | Explicit via constructor |
-| Dynamic content | No (static files only) | Yes (factory delegates) |
-| Reusability | Copy skill directory | Inline or shared instances |
+| Aspect | File-Based | Code-Defined | Class-Based |
+|--------|-----------|--------------|-------------|
+| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# | Classes extending `AgentClassSkill` |
+| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) | `CreateResource` factory methods |
+| Scripts | Supported via script runner delegate | `AddScript` delegates | `CreateScript` factory methods |
+| Discovery | Automatic from directory path | Explicit via constructor | Explicit via constructor |
+| Dynamic content | No (static files only) | Yes (factory delegates) | Yes (factory delegates) |
+| Sharing pattern | Copy skill directory | Inline or shared instances | Package in shared assemblies/NuGet |
+| DI support | No | Yes (via `IServiceProvider` parameter) | Yes (via `IServiceProvider` parameter) |
 
-For single-source scenarios, use the `AgentSkillsProvider` constructors directly. To combine multiple skill types, use the `AgentSkillsProviderBuilder`.
+### `AgentSkillsProvider` vs `AgentSkillsProviderBuilder`
 
+For single-source scenarios, use the `AgentSkillsProvider` constructors directly — they accept a skill directory path, a set of skills, or a custom source.
+
+Use `AgentSkillsProviderBuilder` for advanced scenarios where simple constructors are insufficient:
+
+- **Mixed skill types** — combine file-based, code-defined, and class-based skills in one provider
+- **Multiple file script runners** — use different script runners for different file skill directories
+- **Skill filtering** — include or exclude skills using a predicate
+
+See [Agent_Step04_MixedSkills](Agent_Step04_MixedSkills/) for a working example.

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

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

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

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

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

@@ -15,7 +15,7 @@ using Microsoft.Agents.AI.Foundry;
 
 string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? "memory-store-sample";
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";
 
 // Create an AIProjectClient for Foundry with Azure Identity authentication.

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

@@ -14,7 +14,7 @@ This sample demonstrates how to create and run an agent that uses Microsoft Foun
 ## Prerequisites
 
 1. Azure subscription with Microsoft Foundry project
-2. Azure OpenAI resource with a chat model deployment (e.g., gpt-4o-mini) and an embedding model deployment (e.g., text-embedding-ada-002)
+2. Azure OpenAI resource with a chat model deployment (e.g., gpt-5.4-mini) and an embedding model deployment (e.g., text-embedding-ada-002)
 3. .NET 10.0 SDK
 4. Azure CLI logged in (`az login`)
 
@@ -26,7 +26,7 @@ export AZURE_AI_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api
 export AZURE_AI_MEMORY_STORE_ID="my_memory_store"
 
 # Model deployment names (models deployed in your Foundry project)
-export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"
 export AZURE_AI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-ada-002"
 ```
 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -15,7 +15,7 @@ using OpenAI.Chat;
 using OpenAI.Conversations;
 
 string apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
-string model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";
+string model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-5.4-mini";
 
 // Create a ConversationClient directly from OpenAIClient
 OpenAIClient openAIClient = new(apiKey);

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step05_Conversation/README.md

@@ -69,7 +69,7 @@ foreach (ClientResult result in getConversationItemsResults.GetRawPages())
 1. Set the required environment variables:
    ```powershell
    $env:OPENAI_API_KEY = "your_api_key_here"
-   $env:OPENAI_CHAT_MODEL_NAME = "gpt-4o-mini"
+   $env:OPENAI_CHAT_MODEL_NAME = "gpt-5.4-mini"
    ```
 
 2. Run the sample:

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/Program.cs

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

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/Program.cs

@@ -14,7 +14,7 @@ using OpenAI.Chat;
 using Qdrant.Client;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
 var afOverviewUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/overview/index.md";
 var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/README.md

@@ -23,7 +23,7 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 $env:AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-3-large"  # Optional, defaults to text-embedding-3-large
 ```
 

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/Program.cs

@@ -13,7 +13,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 TextSearchProviderOptions textSearchOptions = new()
 {

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs

@@ -14,7 +14,7 @@ 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-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "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.

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/Program.cs

@@ -8,7 +8,7 @@ using Neo4j.AgentFramework.GraphRAG;
 using Neo4j.Driver;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 var neo4jUri = Environment.GetEnvironmentVariable("NEO4J_URI") ?? throw new InvalidOperationException("NEO4J_URI is not set.");
 var neo4jUsername = Environment.GetEnvironmentVariable("NEO4J_USERNAME") ?? "neo4j";
 var neo4jPassword = Environment.GetEnvironmentVariable("NEO4J_PASSWORD") ?? throw new InvalidOperationException("NEO4J_PASSWORD is not set.");

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/README.md

@@ -15,7 +15,7 @@ The sample uses a Neo4j fulltext index for retrieval and a Cypher `RetrievalQuer
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"
 $env:NEO4J_URI="neo4j+s://your-instance.databases.neo4j.io"
 $env:NEO4J_USERNAME="neo4j"
 $env:NEO4J_PASSWORD="your-password"

dotnet/samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Program.cs

@@ -14,7 +14,7 @@ using OpenAI.Chat;
 using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create a sample function tool that the agent can use.
 [Description("Get the weather for a given location.")]

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/Program.cs

@@ -14,7 +14,7 @@ using SampleApp;
 using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
 string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create chat client to be used by chat client agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/README.md

@@ -28,7 +28,7 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```
 
 ## Run the sample

dotnet/samples/02-agents/Agents/Agent_Step03_PersistedConversations/Program.cs

@@ -11,7 +11,7 @@ using Microsoft.Agents.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create the agent
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/Agents/Agent_Step04_3rdPartyChatHistoryStorage/Program.cs

@@ -18,7 +18,7 @@ using SampleApp;
 using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create a vector store to store the chat messages in.
 // Replace this with a vector store implementation of your choice if you want to persist the chat history to disk.

dotnet/samples/02-agents/Agents/Agent_Step05_Observability/Program.cs

@@ -11,7 +11,7 @@ using OpenTelemetry;
 using OpenTelemetry.Trace;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
 
 // Create TracerProvider with console exporter

dotnet/samples/02-agents/Agents/Agent_Step06_DependencyInjection/Program.cs

@@ -12,7 +12,7 @@ using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.Hosting;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create a host builder that we will register services with and then run.
 HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);

dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/Program.cs

@@ -11,7 +11,7 @@ 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-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/README.md

@@ -22,7 +22,7 @@ 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-4o-mini # Replace with your model deployment name
+    - AZURE_AI_MODEL_DEPLOYMENT_NAME = 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.

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Program.cs

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

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/README.md

@@ -20,7 +20,7 @@ This sample demonstrates how to use image multi-modality with an AI agent. It sh
 Before running this sample, ensure you have:
 
 1. An Azure OpenAI project set up
-2. A compatible model deployment (e.g., gpt-4o)
+2. A compatible model deployment (e.g., gpt-5.4-mini)
 3. Azure CLI installed and authenticated
 
 ## Environment Variables
@@ -29,7 +29,7 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o" # Replace with your model deployment name (optional, defaults to gpt-4o)
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini" # Replace with your model deployment name (optional, defaults to gpt-5.4-mini)
 ```
 
 ## Run the sample

dotnet/samples/02-agents/Agents/Agent_Step09_AsFunctionTool/Program.cs

@@ -10,7 +10,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/Program.cs

@@ -15,7 +15,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Responses;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 var stateStore = new Dictionary<string, JsonElement?>();
 

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/README.md

@@ -24,5 +24,5 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5"  # Optional, defaults to gpt-5
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Program.cs

@@ -15,7 +15,7 @@ using Microsoft.Extensions.AI;
 
 // Get Microsoft Foundry configuration from environment variables
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o";
+var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Get a client to create/retrieve server side agents with
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/README.md

@@ -27,7 +27,7 @@ Attempting to use function middleware on agents that do not wrap a ChatClientAge
 
 1. Environment variables:
    - `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
-   - `AZURE_OPENAI_DEPLOYMENT_NAME`: Chat deployment name (optional; defaults to `gpt-4o`)
+   - `AZURE_OPENAI_DEPLOYMENT_NAME`: Chat deployment name (optional; defaults to `gpt-5.4-mini`)
 2. Sign in with Azure CLI (PowerShell):
    ```powershell
    az login

dotnet/samples/02-agents/Agents/Agent_Step12_Plugins/Program.cs

@@ -17,7 +17,7 @@ using Microsoft.Extensions.DependencyInjection;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create a service collection to hold the agent plugin and its dependencies.
 ServiceCollection services = new();

dotnet/samples/02-agents/Agents/Agent_Step13_ChatReduction/Program.cs

@@ -12,7 +12,7 @@ using Microsoft.Extensions.AI;
 using OpenAI.Chat;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Construct the agent, and provide a factory to create an in-memory chat message store with a reducer that keeps only the last 2 non-system messages.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/Program.cs

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

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/README.md

@@ -23,5 +23,5 @@ Set the following environment variables:
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
 ```

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/Program.cs

@@ -10,7 +10,7 @@ using Microsoft.Agents.AI;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_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-4o";
+var modelDeploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "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.

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/README.md

@@ -13,7 +13,7 @@ Before running this sample, ensure you have:
 
 1. A Microsoft Foundry project set up
 2. A deep research model deployment (e.g., o3-deep-research)
-3. A model deployment (e.g., gpt-4o)
+3. A model deployment (e.g., gpt-5.4-mini)
 4. A Bing Connection configured in your Microsoft Foundry project
 5. Azure CLI installed and authenticated
 
@@ -45,5 +45,5 @@ $env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/pr
 # Optional, defaults to o3-deep-research
 $env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"
 
-# Optional, defaults to gpt-4o
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o"
+# Optional, defaults to gpt-5.4-mini
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-5.4-mini"

dotnet/samples/02-agents/Agents/Agent_Step16_Declarative/Program.cs

@@ -8,7 +8,7 @@ using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // Create the chat client
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Program.cs

@@ -18,7 +18,7 @@ using SampleApp;
 using MEAI = Microsoft.Extensions.AI;
 
 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-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // A sample function to load the next three calendar events for the user.
 Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Program.cs

@@ -16,7 +16,7 @@ using Microsoft.Agents.AI.Compaction;
 using Microsoft.Extensions.AI;
 
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
 
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/README.md

@@ -68,7 +68,7 @@ Order strategies from **least aggressive** to **most aggressive**. The pipeline
 
 ```powershell
 $env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"  # Required
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"                       # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"                      # Optional, defaults to gpt-5.4-mini
 ```
 
 ## Running the Sample
@@ -110,7 +110,7 @@ IEnumerable<ChatMessage> compacted = await CompactionProvider.CompactAsync(
 The `SummarizationCompactionStrategy` accepts any `IChatClient`. Use a smaller, cheaper model to reduce summarization cost:
 
 ```csharp
-IChatClient summarizerChatClient = openAIClient.GetChatClient("gpt-4o-mini").AsIChatClient();
+IChatClient summarizerChatClient = openAIClient.GetChatClient("gpt-5.4-mini").AsIChatClient();
 new SummarizationCompactionStrategy(summarizerChatClient, CompactionTriggers.TokensExceed(4000))
 ```