Task decomposition and sharper prompts reduce variance at the planning layer. Removing validation or increasing temperature would make inconsistency worse, not better. That matters because a tool boundary where every API has declared inputs, declared outputs, validation, retry behavior, and instrumentation.
Together, C states "Identify where dividing the tasks into subtasks and handling them by multiple agents can help."; D states "Refine the prompt given to the AI Agent; be clear on objectives", so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. the combination of Options C and D fits the operating model because the problem describes an agent that must remain adaptive under changing inputs and infrastructure conditions. The alternatives would look simpler in a prototype, but relying on the model to infer API behavior invites fabricated endpoints, malformed arguments, and brittle production behavior. This lines up with NVIDIA guidance because NVIDIA's agent tooling favors explicit function specifications and observable execution paths instead of free-form API narration in the prompt. The result is a system that can be benchmarked, traced, and revised without destabilizing the whole agent fabric.