AI’s Real Bottleneck: Context, Not Compute

AI coding agents burn most of their tokens not on writing code, but on discovering how a large codebase works. CAST’s static analysis replaces that probabilistic discovery with a deterministic dependency graph. In a mid‑size Java app, the graph halved token consumption and doubled accuracy for common tasks.

SoftServe used this com of deterministic context and AI to collapse a 4.5M LOC mainframe modernization from 7.5 years to 3 years, with only two architects. The discovery phase was eliminated; agents started from an up‑to‑date map, focusing tokens on generating tested code and specs.

The approach depends on a proprietary static analysis tool and keeping the graph current. Evidence is strong but vendor‑supplied; the 95% accuracy claim comes from a single case study. Engineering leaders should treat deterministic context as an AI infrastructure investment, shifting costs from per‑query token burn to fixed graph maintenance, and making architecture governance automatable.