MARE started with a simple question: do we really need to embed everything? Instead of chunking and embedding the full dataset, MARE uses a small semantic navigation index and lets an agent investigate data through MongoDB tools. In a 10K-incident benchmark, it used just 604 vectors vs 60,000 for RAG, while scoring 19/20 vs 18/20 on held-out questions. RAG remains faster for simple lookup. MARE explores where agentic retrieval helps on multi-hop, investigative questions.