Spec-Graph Meta-framework for AI-Assisted Software Development: An Industry Case

Abstract

AI-assisted software development increasingly relies on large lan- guage models to transform natural-language specifications into code. However, in most current workflows, specifications are treated as inline prompt content, limiting their structure, reuse, and trace- ability across development tasks. This paper introduces a meta- framework for spec-driven AI-assisted development in which spec- ifications are represented as a typed, queryable graph consumed at runtime by software agents. The framework is organized around three principles: legibility, achieved through a typed hierarchy that distinguishes buildable artifacts from contextual constraints; addressability, enabled by a minimal set of edge types, substrate- agnostic graph materialization, and a cost-function-driven sequenc- ing mechanism; and longevity, maintained through continuous cu- ration of the specification substrate. We instantiate the framework in an industrial setting involving a multi-project software develop- ment organization with over fifty employees, using thirteen speci- fication types, a multi-substrate architecture, and four cooperating AI agents. To explore the proposed framework’s behavior in prac- tice we surveyed 19 practitioners in the organization after using the framework from one to five months.