The selected design maps to Implementing retrieval-augmented generation RAG pipelines combined with vector databases to accelerate access to relevant information, which is the highest-control path for this scenario rather than a prompt-only or single-service shortcut. For knowledge-grounded agents, the clean architecture is a RAG path with retrievers and vector indexes externalized from the LLM, then evaluated for retrieval quality and answer faithfulness. The agent should not infer operational details from latent model knowledge when it can bind to structured tools, retrievers, schemas, and examples. This reduces hallucinated endpoints, malformed parameters, stale facts, and brittle parsing when APIs, documents, or user inputs change. The distractors are weaker because they lean on A: Using traditional relational databases because they don t need specialized retrieval mechanisms...; B: Integrating client data sources as they already incorporate data quality checks or...; C: Relying on pre-trained models instead of connecting to external knowledge sources during..., 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.