Abstract
This study evaluates LLM-based agents for cleaning automotive maintenance logs affected by typos, missing fields, duplicates, and invalid dates. The results show strong performance on general cleaning tasks while highlighting the need for additional domain knowledge when correcting specialized maintenance errors.