The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocol
arXiv:2606.03907v1 Announce Type: cross Abstract: Agentic AI coding tools write code with increasing autonomy and in doing so decide when to import a library and when to implement functionality from scratch. These decisions, whether to build functionality from scratch or buy into an external library, hereafter build-versus-buy, carry direct consequences for software security, licensing compliance, performance, and long-term maintainability. Yet no controlled experimental study has examined what governs build-versus-buy decisions in agentic AI coding tools. Configuration mechanisms, i.e., the m
The proliferation of agentic AI coding tools makes understanding their decision-making processes, particularly build-vs-buy, a critical current research area.
This research directly impacts the security, maintainability, and efficiency of software developed by AI agents, which will become a significant portion of future codebases.
Our understanding of how to configure and control autonomous AI agents' core development decisions is becoming more formalized, moving from implicit to explicit management.
- · Software developers (with better tools)
- · AI software tool vendors
- · Cybersecurity consultancies
- · Companies with poor AI governance strategies
- · Developers resistant to AI integration
Improved configuration mechanisms for AI coding tools will lead to more reliable and secure AI-generated code.
Standardization of these configuration mechanisms could become a new layer of control and competition within the AI development ecosystem.
The ability to finely tune AI's build-vs-buy decisions could lead to distinct 'AI coding styles' or 'AI development frameworks' with measurable performance and security characteristics.
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Read at arXiv cs.AI