Module decomposition cut agent token use 32% on follow-up feature additions (docs.krv.ai)

A recent case study has demonstrated that a Topos-guided structural refactor results in significant cost and time savings for subsequent feature additions in code. In a controlled experiment involving a healthcare claims engine, the follow-on feature sessions required 32.6% fewer tokens and were 22.9% faster after a refactor, although the upfront cleanup incurred higher initial costs. The improvements were attributed to the reduced complexity of the code, allowing the AI model to allocate fewer resources in terms of tokens and time to future tasks. This experiment is crucial for the AI/ML community as it highlights the value of code cleanliness for optimizing AI-driven development. By illustrating that a well-structured codebase can lower the cost of subsequent feature implementation by nearly 25%, it encourages developers and organizations to consider strategic refactoring as a means to enhance overall efficiency. While the findings are specific to this case, they suggest a potentially scalable benefit of using structural feedback mechanisms like Topos to make AI model interactions with code more resource-efficient, ultimately impacting long-term development practices and costs.