Repeatability
High
The transformation rules are consistent and enumerable: normalize casing, deduplicate IDs, parse resolution_time, fix known patterns. This is structurally identical every run and easily scriptable.
Ambiguity Tolerance
High
Success criteria are explicit — numeric resolution_time, canonical priority values, flagged duplicates, valid emails. There is minor ambiguity in category typo correction, but that can be handled with fuzzy matching and a flagging pass rather than judgment calls.
Data & Tool Availability
High
The agent needs only the CSV file and a Python/pandas environment, both of which are standard and readily available. No external APIs, credentials, or live system access are required.
Error Cost
Low
The output is a cleaned CSV for downstream analysis, not a live system write. Errors are easily audited by spot-checking the output, and the original file is untouched, making this fully reversible.
Human Judgment Required
Low
Nearly all transformations are rule-based. The one area requiring light judgment — resolving ambiguous category typos — can be handled by flagging uncertain rows for human review rather than guessing.