Repeatability
High
The structure is identical every run: ingest two CSVs, normalize fields, apply fuzzy matching logic, score matches, and output a categorized master list. This is a textbook repeatable data pipeline with no instance-specific judgment required.
Ambiguity Tolerance
High
Success criteria are explicit: four output categories, confidence scores on matches, and a human-reviewable deduplication recommendation. The agent can determine when the task is complete without subjective interpretation.
Data & Tool Availability
High
Both data sources are already exported as CSVs and handed to the agent — no live API access, credentials, or external system integration is required. The agent needs only standard data processing libraries and fuzzy matching tools.
Error Cost
Low
The output is a flagged recommendation list explicitly intended for human review before any action is taken. No records are deleted or merged automatically, so errors are catchable and reversible at the review stage.
Human Judgment Required
Low
Fuzzy matching thresholds and field normalization rules can be defined algorithmically. The agent surfaces ambiguous matches with confidence scores precisely so humans can make the final call — it doesn't need to make those calls itself.