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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
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</ItemGroup>
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</Project>
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// Copyright (c) Microsoft. All rights reserved.
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// Simplest possible agent evaluation: create a Foundry agent, run it against
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// test questions, and use Foundry quality evaluators to score the responses.
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// For custom domain-specific checks, see the Evaluation_CustomEvals sample.
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using Azure.AI.Projects;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using Microsoft.Extensions.AI.Evaluation;
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using FoundryEvals = Microsoft.Agents.AI.Foundry.FoundryEvals;
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string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-4o-mini";
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// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
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// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
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// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
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AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
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AIAgent agent = projectClient.AsAIAgent(
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model: deploymentName,
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instructions: "You are a helpful assistant. Provide clear, accurate answers.",
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name: "SimpleAgent");
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// Configure Foundry quality evaluators — runs evaluations server-side via the Foundry Evals API.
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FoundryEvals evaluator = new(projectClient, deploymentName, FoundryEvals.Relevance, FoundryEvals.Coherence);
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// Run the agent against test queries and evaluate in one call.
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string[] queries = ["What is photosynthesis?", "How do vaccines work?"];
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AgentEvaluationResults results = await agent.EvaluateAsync(queries, evaluator);
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// Print results.
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Console.WriteLine($"Passed: {results.Passed}/{results.Total}");
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if (results.ReportUrl is not null)
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{
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Console.WriteLine($"Report: {results.ReportUrl}");
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}
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Console.WriteLine();
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for (int i = 0; i < results.Items.Count; i++)
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{
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Console.WriteLine($"Query: {queries[i]}");
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Console.WriteLine($"Response: {(results.InputItems?[i].Response is { } resp ? resp.Substring(0, Math.Min(50, resp.Length)) : "N/A")}...");
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foreach (var metric in results.Items[i].Metrics)
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{
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string score = metric.Value is NumericMetric nm && nm.Value.HasValue
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? nm.Value.Value.ToString("F1")
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: "N/A";
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Console.WriteLine($" {metric.Key}: {score}");
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}
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Console.WriteLine();
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}
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# Evaluation - Simple Eval
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The simplest agent evaluation: create a Foundry agent, run it against test questions, and use Foundry quality evaluators (Relevance, Coherence) to score the responses.
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## What this sample demonstrates
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- Creating an agent with `AIProjectClient.AsAIAgent()`
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- Using `FoundryEvals` with Relevance and Coherence quality evaluators
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- Running evaluation with `agent.EvaluateAsync()` — runs the agent and evaluates in one call
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## Prerequisites
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- .NET 10 SDK or later
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- Azure authentication available to `DefaultAzureCredential` (for local development, run `az login`)
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- A deployed model in your Azure AI Foundry project
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Set the following environment variables:
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```powershell
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$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
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$env:FOUNDRY_MODEL="gpt-4o-mini"
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```
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## Run the sample
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```powershell
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cd dotnet/samples/02-agents/Evaluation
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dotnet run --project .\Evaluation_SimpleEval
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```
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## See also
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- [Evaluation_CustomEvals](../Evaluation_CustomEvals/) — Writing custom domain-specific evaluation checks
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- [Evaluation_ExpectedOutputs](../Evaluation_ExpectedOutputs/) — Evaluating against ground-truth expected outputs
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- [Evaluation_MixedProviders](../../../05-end-to-end/Evaluation/Evaluation_MixedProviders/) — Combining local + Foundry evaluators in one call
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