Repeatability
High
The structure is identical every run: ingest a CSV, cluster similar strings, map variants to canonical names, output a lookup table. This is a textbook data normalization task with no structural variation.
Ambiguity Tolerance
Medium
The output format is clear (lookup table + flagged ambiguities), but deciding which variant is the 'canonical' name requires a judgment call the agent must make consistently. The task wisely offloads the hardest calls to human review, which keeps success criteria workable.
Data & Tool Availability
High
The agent only needs the exported CSV, which is already in hand. No external APIs, live systems, or special permissions are required — just string processing and clustering logic.
Error Cost
Low
The output is a lookup table, not a direct database write, so errors are easy to catch and correct before any downstream impact. The flagging mechanism further reduces risk by surfacing uncertain matches for human sign-off.
Human Judgment Required
Low
Fuzzy matching, case normalization, and abbreviation resolution are well within current AI capability. The genuinely hard cases — where two names might be different entities — are explicitly flagged for human review, so the agent doesn't need to resolve them.