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* dotnet: refresh Foundry sample guidance Carry forward the still-relevant sample guidance and Foundry-specific documentation fixes from the old stacked sample migration work, adapted to the current repo layout and policy. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: rename Foundry sample env vars Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: remove persistent provider sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: drop SAMPLE_GUIDELINES.md from this PR Defer the guidelines doc and its cross-link to a follow-on PR to avoid broken-link failures in CI. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: add DefaultAzureCredential warning to remaining samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * dotnet: address PR review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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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
FoundryEvalswith 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, runaz login) - A deployed model in your Azure AI Foundry project
Set the following environment variables:
$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
cd dotnet/samples/02-agents/Evaluation
dotnet run --project .\Evaluation_SimpleEval
See also
- Evaluation_CustomEvals — Writing custom domain-specific evaluation checks
- Evaluation_ExpectedOutputs — Evaluating against ground-truth expected outputs
- Evaluation_MixedProviders — Combining local + Foundry evaluators in one call