An experiment pitted eleven large language models against a common software engineering challenge: refactoring a monolithic "god node" in a Lang Graph agent. The goal was to see if LLMs could untangle complex logic, moving it from a single function to a more distributed graph structure. This addresses a common anti-pattern that hinders debugging and maintenance.

Models like Fable-5, GPT-5.4, and Gemini-3.1-pro first proposed solutions for the refactoring task. Subsequently, these same models, now acting as evaluators, critiqued each other's proposals. The author employed three distinct methods to gauge model trustworthiness, aiming to determine which LLM excelled at generating valid architectural changes and which could best assess those proposals.

This rigorous comparison tested LLM capabilities beyond simple code generation. It explored their aptitude for architectural reasoning and critical evaluation. The findings offer practical insights into selecting the right LLM for complex code refactoring and analysis, moving beyond mere code completion to more sophisticated engineering tasks.