Denys Linkov examines whether a team was right to spend six months replacing a complicated, multi-repository AI pipeline rather than wait for coding agents to improve. The case frames technical debt as a business tradeoff: faster delivery matters only if complexity, maintainability, reliability, and customer value remain under control.
An early coding-agent attempt completed quickly but made ten major mistakes and required substantial manual intervention. Repeating the same task with newer models reduced the number of iterations and human effort, while a separate zero-shot refactor still produced only scaffolding and failed to implement essential components. The contrast shows why apparent completion is not the same as verified success.
The rebuilt monorepo reached parity in six months, broadened developer participation, reduced pipeline time and cost, handled larger files, and shortened some feature work from months to under a week. The decision lesson is not to refactor every system immediately, but to evaluate current business value, use high reliability thresholds, preserve human review, and verify requirements, builds, behavior, and hidden assumptions.
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