AI 에이전트는 나쁜 데이터도 그냥 쓴다…데이터 정제가 시급한 이유

AI agents fundamentally differ from legacy systems by treating all available data as valid without explicit validation, making data quality a critical prerequisite rather than a future improvement. As autonomous systems scale across enterprises, the cumulative risks of poor data—once manageable with human oversight—become catastrophic; legacy datasets and integration errors can now trigger failures at unprecedented scale. Organizations must prioritize comprehensive data remediation before deploying autonomous systems to prevent irreversible consequences.