Developers and AI‑driven coding agents both stumble when a codebase is tangled. Robert Martin’s Clean Architecture separates a program’s value—functionality and performance—from its structure, the way files, classes and services are arranged. Keeping clean code ensures for all value matters to any stakeholder, poor structure inflates the effort required to add features and introduces bugs that drain time and money in large teams and long‑term projects.

Those same principles apply to large language model (coding agents). LLM context window—how many tokens they can keep in memory—is limited, so a sprawling repository forces the model to read dozens of files, swelling token usage and cost. Moreover, each extra token translates directly into higher cloud expenditure for enterprises. A tidy, modular layout lets an agent touch only a few small files, keeping prompts short and responses reliable.

Practical advice mirrors human habits: when assigning a task, describe the desired folder and naming conventions, not just the functional goal. LLMs tend to mimic the style they see, so a consistently organized repo reduces friction. Review every generated change; without explicit prompts, agents ignore structure. A disciplined code review process catches regressions early, preserving both developer confidence and model efficiency.