The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocol

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A pre-registered study protocol has been developed to investigate how configuration mechanisms influence build-versus-buy decisions in agentic AI coding tools. The study will specifically examine Claude Code and OpenAI Codex, two popular tools that autonomously decide between implementing functionality from scratch or importing external libraries. These decisions have direct consequences for software security, licensing, performance, and maintainability. The protocol involves controlled programming tasks from a benchmark of staged projects, each designed with clear build-versus-buy points. Researchers will manipulate tool configurations, ranging from no configuration to context files, explicit prohibitions, Skills, MCP-enabled library discovery, and permission controls. Measurements will include which libraries are selected, whether new libraries are disclosed, and the accuracy of those disclosures. Nine pre-registered hypotheses structure this protocol, with the resulting benchmark dataset and analysis pipeline to be released as a reusable artifact.

Key takeaway

For Software Engineers and AI Scientists deploying agentic AI coding tools, understanding configuration mechanisms is crucial. Your choices in configuring tools like Claude Code or OpenAI Codex directly impact their build-versus-buy decisions, which carry significant consequences for software security, licensing compliance, and long-term maintainability. You should proactively test and refine configuration strategies, including context files, Skills, and permission controls, to ensure generated code aligns with project requirements and mitigates potential risks.

Key insights

Configuring agentic AI coding tools impacts their build-versus-buy decisions, affecting security and maintainability.

Principles

• Agentic AI coding tools make build-versus-buy decisions autonomously.
• Configuration mechanisms influence AI coding tool behavior.
• Build-versus-buy choices impact security, licensing, and maintainability.

Method

The study protocol involves manipulating configuration mechanisms (e.g., context files, Skills, permission controls) in Claude Code and OpenAI Codex during controlled programming tasks to measure library selection and disclosure.

In practice

• Evaluate AI coding tool configuration for build-versus-buy control.
• Consider security and licensing implications of AI-generated code.
• Utilize benchmark datasets for AI coding tool evaluation.

Topics

• Agentic AI
• AI Coding Tools
• Build-versus-Buy
• Configuration Mechanisms
• Software Security
• Study Protocol

Best for: AI Scientist, Research Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.