In a global financial firm, an AI Architect is building a multi-agent compliance assistant using...

Compliance assistants need both ephemeral turn state and durable policy/user context. NeMo plus vector/graph memory is a better fit than pretending TensorRT stores historical knowledge. That matters because separate short-term context for the current task and long-term memory for preferences, history, and durable domain facts. The selected option specifically A states “Leverage NVIDIA NeMo Framework with modular memory management, integrating conversational state tracking, knowledge graphs, and vector store retrieval, while using LoRA-tuned models to adapt responses overtime.”, which matches the operational requirement rather than a superficial wording match. Option A wins because it optimizes the system boundary around the risky component rather than hoping the base model behaves consistently. The alternatives would look simpler in a prototype, but fine-tuning alone cannot store frequently changing facts, and RAG alone does not train better habitual behavior. The NVIDIA implementation angle is not cosmetic here: NeMo-style training and retrieval workflows distinguish learned behavior from recallable enterprise knowledge. The result is a system that can be benchmarked, traced, and revised without destabilizing the whole agent fabric.