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This study introduces RAMP, a four-level maturity model that evaluates how teams configure AI tools based on version-controlled artifacts, revealing significant variations in adoption practices across 441 repositories. The findings indicate that while coding agents enhance development velocity by 28-38% more commits, those without committed AI configurations experience a much higher increase in cognitive complexity and static-analysis warnings. This suggests that simply adopting coding agents is insufficient; the manner of their integration critically influences software quality and technical debt.
Teams that neglect committed AI configurations face nearly double the cognitive complexity and static-analysis warnings compared to those that do, despite similar gains in development speed.
Coding agents increase development velocity but also technical debt. Prior work reports only average effects across adopters, hiding wide differences between teams. We introduce RAMP (Repository AI Maturity Profile), a four-level cumulative maturity model grounded in version-controlled artifacts that teams commit to configure AI tools. RAMP runs from behavioral rules and coding standards through named agent definitions to multi-agent orchestration, with observed practice concentrated in the first three levels. Across 441 repositories the levels behave as a cumulative scale, and independent human annotation reproduces RAMP's repository-level labels on 97% of a held-out sample. Adoption is cumulative, forward-only, and set-and-forget: 73.8% of artifacts are committed once and never modified. Re-estimating an existing agent-adoption panel within each stratum, agents accelerate development regardless of maturity (28-38% more commits), but quality diverges: among agent-first repositories, where the contrast is identified, those without committed AI configuration show roughly twice the increase in cognitive complexity (+53% versus +27%) and 1.7x the increase in static-analysis warnings. Because maturity is observational, correlated engineering discipline or model capability may explain part of the gap; we present these findings as hypothesis-generating and release RAMP as a reusable instrument.
