An agent is tasked with solving a series of complex mathematical problems that require external...

ReAct is built for tool-using reasoning because each action is followed by an observation. That makes intermediate state visible and reduces arithmetic/tool-use drift. Option A is the correct engineering choice because the requirement is not just “make the model answer,” but control the execution surface. The selected option specifically A states “ReAct”, which matches the operational requirement rather than a superficial wording match. The durable control mechanism is schema-bound tool invocation, typed parameters, timeout envelopes, retry policy, and traceable function execution. In NVIDIA terms, the Agent Toolkit model is to expose tools as reusable workflow components; that is what makes multi-tool agents testable under schema changes. The distractors fail because embedding tools inside the agent loop makes security review, timeout handling, and version control unnecessarily difficult. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity. Schema validation, typed return objects, and trace IDs also make post-incident debugging realistic when a third-party dependency changes behavior.