agent-framework/dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection

Self-Reflection Evaluation with Groundedness Assessment

This sample demonstrates the self-reflection pattern using Agent Framework with Microsoft.Extensions.AI.Evaluation.Quality evaluators. The agent iteratively improves its responses based on real groundedness evaluation scores.

For details on the self-reflection approach, see Reflexion: Language Agents with Verbal Reinforcement Learning (NeurIPS 2023).

What this sample demonstrates

• Self-reflection loop that improves responses using real GroundednessEvaluator scores
• Using RelevanceEvaluator and CoherenceEvaluator for multi-metric quality assessment
• Combining quality and safety evaluators with CompositeEvaluator
• Configuring ContentSafetyServiceConfiguration for safety evaluators alongside LLM-based quality evaluators
• Tracking improvement across iterations

Prerequisites

Before you begin, ensure you have the following prerequisites:

• .NET 10 SDK or later
• Azure AI Foundry project (hub and project created)
• Azure OpenAI deployment (e.g., gpt-4o or gpt-4o-mini)
• Azure CLI installed and authenticated (for Azure credential authentication)

Note: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with az login and have access to the Azure Foundry resource. For more information, see the Azure CLI documentation.

Azure Resources Required

1. Azure AI Hub and Project: Create these in the Azure Portal
   • Follow: https://learn.microsoft.com/azure/ai-foundry/how-to/create-projects
2. Azure OpenAI Deployment: Deploy a model (e.g., gpt-4o or gpt-4o-mini)
   • Agent model: Used to generate responses
   • Evaluator model: Quality evaluators use an LLM; best results with GPT-4o
3. Azure CLI: Install and authenticate with az login

Environment Variables

Set the following environment variables:

$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-project.api.azureml.ms"  # Azure Foundry project endpoint
$env:AZURE_OPENAI_ENDPOINT="https://your-openai.openai.azure.com/"         # Azure OpenAI endpoint (for quality evaluators)
$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini"                   # Model deployment name

Note: For best evaluation results, use GPT-4o or GPT-4o-mini as the evaluator model. The groundedness evaluator has been tested and tuned for these models.

Run the sample

Navigate to the sample directory and run:

cd dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection
dotnet run

Expected behavior

The sample runs three evaluation scenarios:

1. Self-Reflection with Groundedness

• Asks a question with grounding context
• Evaluates response groundedness using GroundednessEvaluator
• If score is below 4/5, asks the agent to improve with feedback
• Repeats up to 3 iterations
• Tracks and reports the best score achieved

2. Quality Evaluation

• Evaluates a single response with multiple quality evaluators:
  • RelevanceEvaluator — is the response relevant to the question?
  • CoherenceEvaluator — is the response logically coherent?
  • GroundednessEvaluator — is the response grounded in the provided context?

3. Combined Quality + Safety Evaluation

• Runs both quality and safety evaluators together:
  • RelevanceEvaluator, CoherenceEvaluator (quality)
  • ContentHarmEvaluator (safety — violence, hate, sexual, self-harm)
  • ProtectedMaterialEvaluator (safety — copyrighted content detection)

Understanding the Evaluation

Groundedness Score (1-5 scale)

The GroundednessEvaluator measures how well the agent's response is grounded in the provided context:

• 5 = Excellent - Response is fully grounded in context
• 4 = Good - Mostly grounded with minor deviations
• 3 = Fair - Partially grounded but includes unsupported claims
• 2 = Poor - Significant amount of ungrounded content
• 1 = Very Poor - Response is largely unsupported by context

Self-Reflection Process

1. Initial Response: Agent generates answer based on question + context
2. Evaluation: GroundednessEvaluator scores the response (1-5)
3. Feedback: If score < 4, agent receives the score and is asked to improve
4. Iteration: Process repeats until good score or max iterations

Best Practices

1. Provide Complete Context: Ensure grounding context contains all information needed to answer the question
2. Clear Instructions: Give the agent clear instructions about staying grounded in context
3. Use Quality Models: GPT-4o recommended for evaluation tasks
4. Multiple Evaluators: Use combination of evaluators (groundedness + relevance + coherence)
5. Batch Processing: For production, process multiple questions in batch

Related Resources

• Reflexion Paper (NeurIPS 2023)
• Microsoft.Extensions.AI.Evaluation Libraries
• GroundednessEvaluator API Reference
• Azure AI Foundry Evaluation Service

Next Steps

After running self-reflection evaluation:

1. Implement similar patterns for other quality metrics (relevance, coherence, fluency)
2. Integrate into CI/CD pipeline for continuous quality assurance
3. Explore the Safety Evaluation sample (FoundryAgents_Evaluations_Step01_RedTeaming) for content safety assessment