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You have a Microsoft Foundry multi-agent solution. The solution includes an orchestrator agent that sends a mix of simple requests, reasoning-heavy tasks, and tool-calling workflows to a single premium model deployment. Response quality is acceptable, but costs are too high, and latency varies significantly across requests. You need to recommend a solution to reduce token utilization based on the complexity of the user input. What should you include in the recommendation?
Correct Answer: A
The workload mixes simple prompts, reasoning-heavy tasks, and tool-calling workflows, so a single premium model is over-provisioned for many requests. Microsoft Foundry ' s model router evaluates request complexity and routes each request to an appropriate model from a configured pool. Simple work can use lower-cost, lower-latency models, while complex reasoning can still be assigned to a more capable model. Structured outputs constrain response format but do not reduce model capability according to request complexity. Prompt Optimizer can improve prompt quality but does not perform per-request model selection, and choosing another flagship model would preserve the same all-requests-use-premium pattern. Therefore A directly addresses the cost and latency variability by matching model capacity to workload complexity. A robust evaluation program separates process metrics from final-response metrics. The selected answer measures the layer where the stated failure actually occurs, which is essential for deciding whether to change retrieval, orchestration, prompt behavior, or the final generator. Official Microsoft reference: Microsoft Foundry - Model router