Repeatability
High
The structure is consistent: fixed input schema, deterministic deduplication and merge logic, rule-based flagging, and a templated output report. This could be re-run on new monthly exports with minimal changes.
Ambiguity Tolerance
High
Success criteria are explicit—single clean CSV, flagged rows for missing regions and malformed SKUs, and a data-quality report with specific metrics. There is little room for interpretation about what 'done' looks like.
Data & Tool Availability
High
The agent needs the six CSVs and the product category reference table, all of which are described as available. Standard Python libraries (pandas) or a code-execution environment are sufficient—no external APIs or live credentials required.
Error Cost
Low
The output is a new CSV, not a write-back to a live system, so errors are easily caught and corrected before downstream use. The flagging step further reduces risk by surfacing uncertain rows for human review rather than silently resolving them.
Human Judgment Required
Low
Deduplication, merging, and category mapping are rule-based. The agent is explicitly instructed to flag ambiguous rows rather than resolve them, which appropriately routes the hard judgment calls to a human.