AI-Readable Architecture: A New Abstraction Layer for AI-Augmented Software Development
Abstract
Contemporary software development increasingly relies on large language model (LLM) agents to generate, review, and modify backend code. However, conventional backend frameworks were designed for human developers who carry architectural knowledge implicitly. This mismatch results in AI-generated code that is syntactically correct but architecturally unsound, violating module boundaries, business invariants, and cross-service contracts. This article introduces the concept of AI-readable architecture as a new abstraction layer for software systems: one in which structural constraints are not inferred by agents from code, but declared as first-class, formally defined, machine-traversable artefacts. We present SysMARA, an open-source TypeScript implementation of this abstraction layer, and provide a formal definition of its central data structure - the AI System Graph G = (V, E, t_v, t_e) - along with three graph properties (Determinism, Impact Completeness, and Constraint Visibility) and a formal definition of architectural violation as a measurable construct. We report two empirical studies conducted on SysMARA v0.7.1: (1) a controlled experiment in which Claude generated code across five tasks in two conditions - vanilla Express and SysMARA - yielding violation rates of 50% and 0% respectively; and (2) a multi-module case study demonstrating cross-module invariant enforcement, formal module boundary validation, and complete constraint queryability. The article compares the approach against established alternatives, discusses implications and limitations, and identifies future research directions.