Retrieval-augmented generation vs. deterministic tax computation in multi-agent financial advisory: A 2x2 factorial experiment

The study compares a custom capital gains calculation engine with a retrieval‑augmented generation (RAG) vector store of market advisory reports in a multi‑agent financial advisory system. A 2x2 factorial experiment showed that the tax‑optimization engine significantly reduced tax savings, while the RAG component had no significant effect. The RAG‑only condition yielded the highest tax savings, suggesting that pretrained language model knowledge may suffice for tax‑loss harvesting without specialized tooling.

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• llms
• rag
• agents