Repeatability
High
The task structure is identical every run: ingest CSV, apply normalization and fuzzy-match logic, output deduplicated records with merge flags. This is a textbook repeatable data pipeline with no meaningful variation in approach.
Ambiguity Tolerance
Medium
The output format and goal are clearly defined, but the acceptable similarity threshold for merging records (e.g., how close is 'close enough' for a typo vs. a different person) requires a judgment call that the user hasn't fully specified. A reasonable default can be applied, but edge cases may need human review.
Data & Tool Availability
High
The agent only needs the CSV file and standard libraries (pandas, recordlinkage, fuzzywuzzy, or similar). No external APIs, credentials, or live systems are required — everything needed is self-contained.
Error Cost
Medium
Incorrectly merging two distinct customers (false positives) or failing to merge obvious duplicates (false negatives) can corrupt CRM data or cause downstream operational issues. However, the output is a new file — the original data is preserved — making errors detectable and reversible with a human review step.
Human Judgment Required
Low
The vast majority of deduplication decisions are algorithmic: normalize case and whitespace, compute string similarity, apply a threshold. Genuine ambiguity (e.g., two people named John Smith with similar emails) is a small fraction of 8,000 records and can be flagged for human review rather than blocking automation.