LLM math vs a deterministic finance API
For valuation, pricing, and risk, route the math to a deterministic API; keep the LLM for orchestration. A model that 'usually gets the IRR close' is a liability where a wrong number flips a decision.
Letting the LLM compute it, Calling a deterministic endpoint
Letting the LLM compute it
Asking the model directly is zero-integration and feels fast: no extra call, no payment. It fails on anything requiring a solver or special function — IRR, XIRR, yield-to-maturity, Black-Scholes — where the model returns a confident but wrong number, varies between runs, and offers no evidence for how it got there. Under audit, that is indefensible.
Calling a deterministic endpoint
A deterministic endpoint is a pure function: identical inputs return identical, audited outputs with per-rule findings. It costs one extra round trip and a few cents per call over x402, and requires the agent to format inputs. In exchange the number is correct, reproducible, and explainable — which is exactly what financial decisions require.
Recommendation
For valuation, pricing, and risk, route the math to a deterministic API; keep the LLM for orchestration. A model that 'usually gets the IRR close' is a liability where a wrong number flips a decision.
How to combine
Most teams let the LLM orchestrate and route the actual finance math to deterministic endpoints, paying per call over x402. Start with the agent services page and the OpenAPI spec to wire the first endpoint into your agent.