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