You have a Microsoft Foundry project that contains an agent.

The correct answer is B. Add a reflection pass before the responses are returned . A reflection pass is an application-orchestration step in which the generated summary is reviewed before final delivery, typically by asking the model or an evaluator step to check whether the answer covers the retrieved policy evidence and to revise the response when important details are missing. This directly addresses response completeness in application logic before the response is returned. The Microsoft Learn study guide explicitly includes Implement model reflection and Apply prompt engineering techniques to improve responses under optimization and operationalization of generative AI solutions.

This is also consistent with Microsoft Foundry agentic-loop guidance, which identifies reflection and planning cycles as patterns for multi-step reasoning in production agent systems. Completeness is a response-quality property: Azure AI evaluation defines completeness as whether a response contains all necessary and relevant information with respect to ground truth.

Option C is not correct because the scenario already says the agent generates summaries from retrieved policy documents, which is already a grounded retrieval pattern. Option A mainly reduces randomness, not missing content. Option D improves delivery experience, not answer completeness. Reference topics: model reflection, prompt engineering, agentic loops, response evaluation, and grounded generative AI solutions.