Repeatability
High
The transformation rules — normalize dates, deduplicate, flag missing fields — are structurally identical every time this spreadsheet is processed. This is a textbook repeatable data pipeline task.
Ambiguity Tolerance
Medium
Most success criteria are crisp (consistent dates, no duplicate rows, flagged empty billing rates), but 'duplicate' is slightly ambiguous — a candidate legitimately placed twice at different clients is not a true duplicate. The agent needs a defined deduplication key or must flag edge cases for human review.
Data & Tool Availability
High
The input is a single spreadsheet with named columns — no external APIs, live systems, or permissions required. A data agent with file access and a Python/pandas environment has everything it needs.
Error Cost
Medium
Errors in the output CSV could cause billing mistakes downstream, but the output is a file — not a live system write — so a human can review before importing. Reversibility is high as long as the original spreadsheet is preserved.
Human Judgment Required
Low
The task is almost entirely rule-based. The only judgment call is resolving ambiguous duplicates (same person, different placements), which the agent can handle by flagging rather than auto-resolving.