The selected design maps to Orchestrate NeMo agents via Triton use vector memory for shared context ReAct planning and NeMo Guardrails for reasoning, which is the highest-control path for this scenario rather than a prompt-only or single-service shortcut. The NVIDIA stack component that anchors this design is NeMo Guardrails, because rails can be placed before retrieval, during dialog, around tool execution, and after generation. Agentic systems need explicit decomposition: a planner or coordinator defines the work, specialized agents or tools execute bounded actions, and memory/state is preserved only where it improves the next decision. That structure increases maintainability because each agent role, message contract, and state transition can be tested independently under load. The distractors are weaker because they lean on B: Use stateless LLM endpoints behind an API gateway and pass shared prompts...; C: Use LangChain to coordinate third-party agent APIs and store shared information in...; D: Fine-tune separate NeMo models for each agent role using LoRA with pre-scripted..., which compromises traceability, resilience, scalability, or policy enforcement in production. The answer therefore fits NVIDIA's production-agent pattern: modular workflow design, measurable runtime behavior, GPU-aware serving where applicable, and controlled integration with enterprise systems.