A team building a loan-inquiry assistant in Microsoft Foundry needs the workflow to check the model's confidence in its answer and, when confidence is low, route the conversation to a human loan officer instead of returning an uncertain answer to the applicant. Which capability should they use to implement this branching behavior between the retrieval step and the final response?

The workflow needs explicit branching between retrieval and the final response. The visual, node-based orchestration flow with a conditional branch is correct because it lets the team wire together discrete steps, retrieval, model call, and a conditional branch, so the confidence output from one step can be evaluated and used to route the conversation down one of two paths: returning the model's answer or handing off to a human loan officer. That explicit branching logic is what the scenario requires. The single long prompt that asks the model to mention its own confidence level in the same reply is incorrect because that reply still goes straight to the applicant either way, and it does not actually intercept low-confidence cases and redirect them to a human before the applicant sees an uncertain answer. The larger context window setting is incorrect because it affects how much input text the model can process, not whether the workflow branches based on a confidence check. The higher temperature setting is incorrect because raising temperature increases the variety of wording the model generates, which does not create a decision point that can detect low confidence and escalate to a person; it would make uncertain answers more varied, not routed away from the applicant.