Governed Software Construction for Large Language Models: Capability-Bounded Mutation, Dual-Gate Verification, and Evidence-Aware State Transitions

Abstract

Large Language Models are evolving from code-completion tools into autonomous software agents capable of inspecting repositories, modifying multiple files, invoking tools, executing tests, and iteratively repairing software systems. This increased capability also creates a governance problem: contemporary coding agents are often granted mutation authority far broader than the defect or requirement they are intended to address. This paper extends Generation–Execution Separation from runtime isolation to construction-time governance. It introduces ANS, a governed software construction model in which an LLM may understand the broader system but may mutate only an explicitly authorized subset of its artifacts. Software construction is modeled as a sequence of bounded, evidence-aware state transitions in which the model possesses proposal authority while deterministic infrastructure retains validation and commit authority. The framework introduces four principal mechanisms: Architecture as Capability Boundary, which converts architectural structure into enforceable dependency and modification permissions; Scoped Mutation, which assigns each construction task a finite mutation capability set; Dual-Gate Governance, which separates static structural verification from dynamic execution evidence; and the Freeze–Invalidation Protocol, which protects previously verified artifacts while deterministically invalidating downstream evidence after authorized upstream changes. The paper organizes software artifacts into five construction categories—Model, Provider, Service, Pipeline, and Interface—and distinguishes static dependency topology from runtime control flow. It further argues that these governance restrictions do not inherently reduce computational expressiveness: a Pipeline language supporting assignment, sequential composition, conditional branching, and unbounded iteration can embed a standard WHILE computation model, while effectful Service composition admits a Kleisli-style semantic interpretation under a selected effect model. The central principle is: Knowledge may be global; mutation authority should be local. Under ANS, AI-generated code is not accepted merely because it can execute. A candidate change becomes part of the trusted software state only after satisfying its authorized mutation scope, structural constraints, and required execution evidence.