AI coding agents are increasingly deployed with broad autonomy over large contexts. In systems whose correctness depends on cross-module guarantees, such as regulated event- driven platforms and broker libraries over external protocols, the properties that matter most are not local to any file an agent edits: idempotent effects, ordered consumption, erasure against statutory retention, audit completeness, capability claims. The Proofload Method restricts agent scope instead of extending it. Work proceeds through a staged pipeline of typed artifacts, and agents implement only inside reviewed handoffs. Four mechanisms keep the artifact corpus in agreement: per-slice freezing of contracts over typed ports and single-producer events; a mechanical consistency checker that fails the build on cross-artifact drift and grows by promoting recurring defect classes to rules; immutable adversarial review records; and a status ledger restricted to claims backed by named evi- dence. The method is accompanied by working hypotheses grounded in known properties of large language models, a reference implementation with a worked example and a mutation- tested checker, and observations from two instantiations: a confidential greenfield build, and a public refounding of an agent-built Redis Streams library. The cases provide feasi- bility evidence and a bounded brownfield observation; controlled evaluation remains future work.
Housseyn Guettaf (2026) studied this question.