Repeatability
High
The structure is identical every run: ingest CSV, classify merchant names, aggregate by customer and month, output enriched CSV plus summary table. This is a deterministic pipeline with no instance-by-instance variation in logic.
Ambiguity Tolerance
Medium
The output format and aggregation logic are well-defined, but the category taxonomy (SaaS vs. professional services vs. ecommerce) is not fully specified and some merchant names will be genuinely ambiguous. A human needs to define or approve the category schema upfront, but once set, success criteria are measurable.
Data & Tool Availability
High
The agent needs only the CSV file and a Python/pandas environment with an LLM or lookup-based classifier for merchant names — all standard, readily available tooling. No external APIs or live credentials are required.
Error Cost
Low
Miscategorized merchants produce an incorrect enriched CSV, but the output is fully reversible — rerun with a corrected classifier and regenerate. No financial transactions are modified; this is purely analytical output.
Human Judgment Required
Low
The aggregation and pattern-finding logic is mechanical. Merchant classification has edge cases, but an LLM-backed classifier handles the vast majority correctly, and a human can audit a sample of the category mappings rather than reviewing every row.