chore: import upstream snapshot with attribution

dotnet/samples/02-agents/Evaluation/Evaluation_CustomEvals/Evaluation_CustomEvals.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Evaluation/Evaluation_CustomEvals/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates writing custom evaluation functions for domain-specific
+// checks. Custom evaluators run locally — no cloud evaluator service needed.
+// For LLM-based quality scoring (relevance, coherence), see Evaluation_SimpleEval.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = projectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are a customer support agent. Help users resolve their issues "
+                + "politely and provide clear, actionable steps.",
+    name: "SupportAgent");
+
+// Custom check: the agent should not refuse to help.
+EvalCheck noRefusal = FunctionEvaluator.Create("no_refusal", (string response) =>
+    !response.Contains("I can't help", StringComparison.OrdinalIgnoreCase)
+    && !response.Contains("I'm unable to", StringComparison.OrdinalIgnoreCase)
+    && !response.Contains("outside my scope", StringComparison.OrdinalIgnoreCase));
+
+// Custom check: response should include actionable guidance (numbered steps or bullet points).
+EvalCheck hasActionableSteps = FunctionEvaluator.Create("has_actionable_steps", (string response) =>
+    response.Contains("1.", StringComparison.Ordinal)
+    || response.Contains("- ", StringComparison.Ordinal)
+    || response.Contains("• ", StringComparison.Ordinal));
+
+// Custom check: response should be substantial but not excessively long.
+EvalCheck reasonableLength = FunctionEvaluator.Create("reasonable_length", (string response) =>
+    response.Length >= 50 && response.Length <= 2000);
+
+// Combine all custom checks into a local evaluator.
+LocalEvaluator evaluator = new(noRefusal, hasActionableSteps, reasonableLength);
+
+string[] queries =
+[
+    "My order hasn't arrived after two weeks. What should I do?",
+    "I was charged twice for the same item. Can you help?",
+    "How do I return a damaged product?",
+];
+
+AgentEvaluationResults results = await agent.EvaluateAsync(queries, evaluator);
+
+Console.WriteLine($"Passed: {results.Passed}/{results.Total}");
+Console.WriteLine();
+
+for (int i = 0; i < results.Items.Count; i++)
+{
+    Console.WriteLine($"Query: {queries[i]}");
+    Console.WriteLine($"Response: {(results.InputItems?[i].Response is { } resp ? resp.Substring(0, Math.Min(50, resp.Length)) : "N/A")}...");
+    foreach (var metric in results.Items[i].Metrics)
+    {
+        string status = metric.Value.Interpretation?.Failed == true ? "FAIL" : "PASS";
+        Console.WriteLine($"  [{status}] {metric.Key}");
+    }
+
+    Console.WriteLine();
+}

dotnet/samples/02-agents/Evaluation/Evaluation_CustomEvals/README.md

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+# Evaluation - Custom Evals
+
+This sample demonstrates writing custom domain-specific evaluation functions using `FunctionEvaluator.Create`. Custom evaluators run locally with no cloud evaluator service needed — useful for enforcing business rules, format requirements, or safety guardrails.
+
+## What this sample demonstrates
+
+- Writing custom checks with `FunctionEvaluator.Create` for domain-specific logic
+- Checking that a customer support agent doesn't refuse to help
+- Verifying responses contain actionable steps (numbered lists or bullet points)
+- Enforcing response length constraints
+- Combining multiple custom checks into a `LocalEvaluator`
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure authentication available to `DefaultAzureCredential` (for local development, run `az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/Evaluation
+dotnet run --project .\Evaluation_CustomEvals
+```
+
+## See also
+
+- [Evaluation_SimpleEval](../Evaluation_SimpleEval/) — Simplest evaluation using Foundry quality evaluators (Relevance, Coherence)
+- [Evaluation_ExpectedOutputs](../Evaluation_ExpectedOutputs/) — Evaluating against ground-truth expected outputs
+- [Evaluation_MixedProviders](../../../05-end-to-end/Evaluation/Evaluation_MixedProviders/) — Combining custom + Foundry evaluators in one call

dotnet/samples/02-agents/Evaluation/Evaluation_ExpectedOutputs/Evaluation_ExpectedOutputs.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Evaluation/Evaluation_ExpectedOutputs/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates evaluating agent responses against expected outputs.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";
+
+// Create a math tutor agent.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You are a math tutor. Answer concisely with the numeric result.",
+        name: "MathTutor");
+
+// Combine built-in checks.
+LocalEvaluator localEvaluator = new(
+    EvalChecks.ContainsExpected(),   // response must contain the expected answer
+    EvalChecks.NonEmpty());          // response must not be empty
+
+// Queries and expected outputs.
+string[] queries = ["What is 2 + 2?", "What is the square root of 144?"];
+string[] expectedOutputs = ["4", "12"];
+
+// Run the agent and evaluate with expected outputs.
+AgentEvaluationResults results = await agent.EvaluateAsync(
+    queries,
+    localEvaluator,
+    expectedOutput: expectedOutputs);
+
+// Print results.
+Console.WriteLine($"Evaluation: {results.ProviderName}");
+Console.WriteLine($"  Passed: {results.Passed}/{results.Total}");
+Console.WriteLine($"  All passed: {results.AllPassed}");
+Console.WriteLine();
+
+for (int i = 0; i < results.Items.Count; i++)
+{
+    Console.WriteLine($"Query: {queries[i]}  |  Expected: {expectedOutputs[i]}");
+    Console.WriteLine($"Response: {(results.InputItems?[i].Response is { } resp ? resp.Substring(0, Math.Min(50, resp.Length)) : "N/A")}");
+    foreach (var metric in results.Items[i].Metrics)
+    {
+        string status = metric.Value.Interpretation?.Failed == true ? "FAIL" : "PASS";
+        Console.WriteLine($"  [{status}] {metric.Key}: {metric.Value.Interpretation?.Reason}");
+    }
+
+    Console.WriteLine();
+}

dotnet/samples/02-agents/Evaluation/Evaluation_ExpectedOutputs/README.md

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+# Evaluation - Expected Outputs
+
+This sample demonstrates evaluating agent responses against expected outputs using built-in checks.
+
+## What this sample demonstrates
+
+- Using `EvalChecks.ContainsExpected` for ground-truth comparison
+- Using `EvalChecks.NonEmpty` for basic response validation
+- Passing `expectedOutput` to `agent.EvaluateAsync()` so checks can access ground truth
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/Evaluation
+dotnet run --project .\Evaluation_ExpectedOutputs
+```
+
+## See also
+
+- [Evaluation_SimpleEval](../Evaluation_SimpleEval/) — Simplest evaluation with built-in and custom checks
+- [Evaluation_FoundryQuality](../../../05-end-to-end/Evaluation/Evaluation_FoundryQuality/) — Cloud-based quality evaluation with Foundry evaluators
+- [Evaluation_FoundryRubric](../../../05-end-to-end/Evaluation/Evaluation_FoundryRubric/) — Rubric (adaptive) evaluators with per-dimension scores

dotnet/samples/02-agents/Evaluation/Evaluation_Multimodal/Evaluation_Multimodal.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Evaluation/Evaluation_Multimodal/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates that the evaluation pipeline preserves multimodal content.
+// When an agent conversation includes images, EvalChecks.HasImageContent() can verify
+// they survived into the EvalItem — useful for testing vision-capable agents.
+//
+// No Azure credentials needed: this sample builds EvalItems locally to show the pattern.
+
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+// Simulate a vision agent conversation where the user sends an image.
+// Just pass the conversation — query/response are derived automatically.
+// For cloud-based quality evaluation of multimodal conversations, see the
+// 05-end-to-end/Evaluation samples (FoundryQuality, ConversationSplits).
+EvalItem imageItem = new(
+    conversation:
+    [
+        new(ChatRole.User,
+        [
+            new TextContent("What do you see in this image?"),
+            new UriContent(new Uri("https://example.com/mountain.png"), "image/png"),
+        ]),
+        new(ChatRole.Assistant, "The image shows a mountain landscape with snow-capped peaks."),
+    ]);
+
+// Simulate a text-only conversation (no image).
+EvalItem textItem = new(
+    query: "Tell me about mountains.",
+    response: "Mountains are large landforms that rise above the surrounding terrain.");
+
+// HasImageContent() passes when the conversation contains an image, fails otherwise.
+// This lets you verify that your vision agent actually received the image.
+LocalEvaluator evaluator = new(
+    EvalChecks.HasImageContent(),
+    EvalChecks.NonEmpty());
+
+AgentEvaluationResults results = await evaluator.EvaluateAsync([imageItem, textItem]);
+
+Console.WriteLine($"Evaluation: {results.Passed}/{results.Total} passed");
+Console.WriteLine();
+
+Console.WriteLine($"Image conversation: has_image_content = {imageItem.HasImageContent}");  // true
+Console.WriteLine($"Text conversation:  has_image_content = {textItem.HasImageContent}");   // false
+Console.WriteLine();
+
+for (int i = 0; i < results.Items.Count; i++)
+{
+    Console.WriteLine($"Item {i + 1}: {results.InputItems![i].Query}");
+    foreach (var metric in results.Items[i].Metrics)
+    {
+        string status = metric.Value.Interpretation?.Failed == true ? "FAIL" : "PASS";
+        Console.WriteLine($"  [{status}] {metric.Key}: {metric.Value.Interpretation?.Reason}");
+    }
+
+    Console.WriteLine();
+}

dotnet/samples/02-agents/Evaluation/Evaluation_Multimodal/README.md

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+# Evaluation - Multimodal
+
+This sample demonstrates that the evaluation pipeline preserves multimodal content. When conversations include images, `EvalChecks.HasImageContent` can verify they survived into the `EvalItem`.
+
+## What this sample demonstrates
+
+- Building `EvalItem` objects with `UriContent` image content
+- Using built-in `EvalChecks.HasImageContent` to detect images in conversations
+- Comparing image vs. text-only conversations to show when the check passes/fails
+- Evaluating directly with `LocalEvaluator.EvaluateAsync()` (no agent needed)
+
+## Prerequisites
+
+- .NET 10 SDK or later
+
+No Azure credentials or environment variables are required for this sample since it evaluates locally without calling an agent.
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/Evaluation
+dotnet run --project .\Evaluation_Multimodal
+```
+
+## See also
+
+- [Evaluation_SimpleEval](../Evaluation_SimpleEval/) — Simplest evaluation with built-in checks and `agent.EvaluateAsync()`
+- [Evaluation_FoundryQuality](../../../05-end-to-end/Evaluation/Evaluation_FoundryQuality/) — Cloud-based quality evaluation with Foundry evaluators
+- [Evaluation_FoundryRubric](../../../05-end-to-end/Evaluation/Evaluation_FoundryRubric/) — Rubric (adaptive) evaluators with per-dimension scores
+- [Evaluation_ConversationSplits](../../../05-end-to-end/Evaluation/Evaluation_ConversationSplits/) — Multi-turn conversation split strategies

dotnet/samples/02-agents/Evaluation/Evaluation_SimpleEval/Evaluation_SimpleEval.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Evaluation/Evaluation_SimpleEval/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+// Simplest possible agent evaluation: create a Foundry agent, run it against
+// test questions, and use Foundry quality evaluators to score the responses.
+// For custom domain-specific checks, see the Evaluation_CustomEvals sample.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI.Evaluation;
+using FoundryEvals = Microsoft.Agents.AI.Foundry.FoundryEvals;
+
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = projectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are a helpful assistant. Provide clear, accurate answers.",
+    name: "SimpleAgent");
+
+// Configure Foundry quality evaluators — runs evaluations server-side via the Foundry Evals API.
+FoundryEvals evaluator = new(projectClient, deploymentName, FoundryEvals.Relevance, FoundryEvals.Coherence);
+
+// Run the agent against test queries and evaluate in one call.
+string[] queries = ["What is photosynthesis?", "How do vaccines work?"];
+AgentEvaluationResults results = await agent.EvaluateAsync(queries, evaluator);
+
+// Print results.
+Console.WriteLine($"Passed: {results.Passed}/{results.Total}");
+if (results.ReportUrl is not null)
+{
+    Console.WriteLine($"Report: {results.ReportUrl}");
+}
+
+Console.WriteLine();
+
+for (int i = 0; i < results.Items.Count; i++)
+{
+    Console.WriteLine($"Query: {queries[i]}");
+    Console.WriteLine($"Response: {(results.InputItems?[i].Response is { } resp ? resp.Substring(0, Math.Min(50, resp.Length)) : "N/A")}...");
+    foreach (var metric in results.Items[i].Metrics)
+    {
+        string score = metric.Value is NumericMetric nm && nm.Value.HasValue
+            ? nm.Value.Value.ToString("F1")
+            : "N/A";
+        Console.WriteLine($"  {metric.Key}: {score}");
+    }
+
+    Console.WriteLine();
+}

dotnet/samples/02-agents/Evaluation/Evaluation_SimpleEval/README.md

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+# Evaluation - Simple Eval
+
+The simplest agent evaluation: create a Foundry agent, run it against test questions, and use Foundry quality evaluators (Relevance, Coherence) to score the responses.
+
+## What this sample demonstrates
+
+- Creating an agent with `AIProjectClient.AsAIAgent()`
+- Using `FoundryEvals` with Relevance and Coherence quality evaluators
+- Running evaluation with `agent.EvaluateAsync()` — runs the agent and evaluates in one call
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure authentication available to `DefaultAzureCredential` (for local development, run `az login`)
+- A deployed model in your Azure AI Foundry project
+
+Set the following environment variables:
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/Evaluation
+dotnet run --project .\Evaluation_SimpleEval
+```
+
+## See also
+
+- [Evaluation_CustomEvals](../Evaluation_CustomEvals/) — Writing custom domain-specific evaluation checks
+- [Evaluation_ExpectedOutputs](../Evaluation_ExpectedOutputs/) — Evaluating against ground-truth expected outputs
+- [Evaluation_MixedProviders](../../../05-end-to-end/Evaluation/Evaluation_MixedProviders/) — Combining local + Foundry evaluators in one call