Repositories with rules files saw roughly half the complexity increase

The authors built RAMP, a four-level maturity scale for what a repository has actually committed to version control to configure an AI tool: nothing (Level 1), context files such as rules or coding standards (Level 2), named agents and reusable commands (Level 3), or multi-agent orchestration (Level 4). Across 441 corporate repositories from 27 organizations, independent human annotators agreed with the automated classifier on the repository's maturity level 97% of the time. Applying that classifier to an existing panel of 509 open-source repositories that adopted coding agents, the authors report that in repositories where an agent was the first AI tool used, agents increased commits regardless of maturity, but quality outcomes split: cognitive complexity rose 52.7% in repositories with no committed configuration versus 26.7% in repositories with at least a Level 2 setup, and static-analysis warnings rose 24.1% versus 14.0%. Even the configured repositories still saw complexity rise by more than a quarter. The paper's discussion says structured practices reduce quality degradation rather than eliminate it. The paper also reports 73.8% of these configuration files are committed once and never revised afterward, and it frames the quality findings as hypothesis-generating rather than causal, since which repositories bother to write configuration is not randomly assigned.

Why it matters: This gives a number to something that has mostly been argued anecdotally: whether a team has written down a rules file for its agent before turning it loose correlates with roughly half the complexity cost, not a marginal difference. The result describes mitigation: the configured group still saw complexity rise 26.7%. Because the paper's own data show teams write these files once and rarely touch them again, getting the first version right, before an agent starts working in the repository, appears to matter more than iterating on it later.