The Economic Benefit of Refactoring in Agentic Engineering
I built a sophisticated application entirely using AI agents, only to discover a massive 17,000-line file in the data access layer. By systematically refactoring this code, I conducted an experiment to measure token consumption for future changes. The results were striking: breaking down the monolithic file reduced input token costs by 83%, proving that strategic refactoring significantly lowers the ongoing expense of working with AI agents.
The saving is not because there is less code to read, but because the agent must be able to successfully identify the smallest subset of files necessary to read.