You are implementing multi-agent RAG so a research agent and a compliance agent share enterprise knowledge with high retrieval precision. Which design choices are most appropriate?
Select an answer to reveal the explanation.
Short Explanation
Correct answer: C. Multi-agent RAG quality is mostly kitchen prep: chunk sensibly, embed well, filter by metadata so compliance doesn’t get marketing fluff, and retrieve with thresholds/rerank—not “stuff 50 random chunks and pray.”
Full Explanation
Multi-agent RAG architecture includes chunking, embedding quality, and retrieval precision, plus knowledge integration for multi-agent consumption. Option C covers chunking, embeddings, metadata-aware filtering, and precision-oriented retrieval suitable when multiple agents share a knowledge plane.
Option A yields poor embedding granularity and noisy context.
Option B is not RAG and does not scale.
Option D is operationally risky and lacks evaluation discipline.
Exam tip: Precision for multi-agent RAG often includes metadata routing so each agent’s retrieval is purpose-scoped.