arXiv:2606.13298v1 Announce Type: cross Abstract: AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding". Yet causal evidence on their effect on software architecture is scarce. Prior causal work has measured code-level outcomes (complexity, static analysis warnings); whether such degradation propagates to architecture-level outcomes remains unknown. We mine 151 open-source Java repositories, 74 with detectable agentic AI adoption (identified via configuration files and Co-Authored-By commit traile
The proliferation of AI coding tools and 'vibe coding' practices has created an urgent need to empirically assess their architectural consequences as adoption matures.
Architectural quality is fundamental to software maintainability, scalability, and security, directly impacting technology companies' long-term viability and operational costs.
This study provides initial causal evidence on how agentic AI adoption might degrade architectural quality, shifting the conversation from code-level metrics to systemic impacts.
- · Software quality assurance platforms
- · Architectural review specialists
- · Developer tooling focused on architectural linting
- · Organizations with lax code review processes
- · Developers solely relying on 'vibe coding'
- · Legacy systems attempting to integrate AI-generated code
Increased technical debt and maintenance burden in applications developed with widespread agentic AI use.
Demand for new tools and methodologies to monitor and remediate architectural degradation caused by AI-assisted development.
Potential for a divergence in software quality between companies deeply integrating AI coding agents without guardrails and those maintaining rigorous human oversight.
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Read at arXiv cs.AI