the real value proposition here is correctness guarantees that LLMs fundamentally cant provide. when an LLM says 2+2=4 it arrived there statistically, not computationally. for anything safety-critical - engineering tolerances, drug dosage calculations, financial modeling - you want a deterministic engine producing the answer and the LLM just translating between human intent and formal queries.

the CAG framing is clever marketing but the underlying idea is sound: treat the LLM as a natural language interface to a computational kernel rather than the computation itself. weve been doing something similar with python subprocess calls from agent pipelines and it works well. the question is whether wolfram language offers enough over python+scipy+sympy to justify the licensing cost and ecosystem lock-in.