Repeatability
High
The transformations are structurally identical across all rows: normalize whitespace, reformat phone numbers, add state codes, standardize URLs, and map categories. This is a classic ETL pattern with no row-by-row unique judgment required.
Ambiguity Tolerance
Medium
Phone normalization and URL standardization have crisp rules, but inferring missing state abbreviations from partial addresses and mapping vague or misspelled categories to a taxonomy introduces real ambiguity. Success criteria are mostly clear, but edge cases need a defined fallback (e.g., flag unresolvable rows rather than guess).
Data & Tool Availability
High
The agent needs the Excel file, the standard taxonomy, and optionally a geocoding or address-validation API for state inference. All of these are either user-supplied or readily accessible — no proprietary system access required.
Error Cost
Low
The output is a CSV that a human can review before use; no irreversible action is taken. Errors are visible and correctable, and the original Excel file remains untouched as a source of truth.
Human Judgment Required
Low
The vast majority of transformations are rule-based. The only genuine judgment calls are ambiguous category mappings and addresses where state cannot be inferred — both of which can be flagged for human review rather than silently guessed.