Repeatability
High
The task is structurally identical for every row: read an issue_type string, match it to one of 8 defined categories, assign a confidence score. No row requires unique judgment beyond the classification logic itself.
Ambiguity Tolerance
High
Success criteria are crisp: every row gets a standardized_category and a confidence score, and the output is a CSV. The user defines the 8 categories, so the mapping target is fixed — ambiguity is bounded and manageable.
Data & Tool Availability
High
The agent needs only the Google Sheet export (or direct Sheets API access) and the 8 category definitions from the user. No external APIs, live data, or special permissions are required beyond file read access.
Error Cost
Low
The output is a new CSV that doesn't overwrite anything, and the confidence scores flag uncertain rows for human review. Misclassifications are easily caught and corrected before the data is used downstream.
Human Judgment Required
Low
Synonym resolution and fuzzy text matching are core LLM strengths. Edge cases like 'charged twice' vs 'double charge' are exactly the kind of semantic equivalence AI handles well. A human should spot-check low-confidence rows, but the bulk of the work needs no human intuition.