Repeatability
High
The operations — normalize strings, geocode cities, deduplicate rows, export CSV — are structurally identical for every record. This is exactly the kind of mechanical, rule-driven data pipeline that agents handle reliably.
Ambiguity Tolerance
Medium
The end goal (clean CSV with lat/long, no duplicates) is crisp, but deduplication logic requires a definition of 'duplicate' (exact match vs. fuzzy match on shipper+consignee+date?) that the user hasn't fully specified. A human needs to confirm the dedup rules before the agent runs.
Data & Tool Availability
High
The agent needs the Excel file and a geocoding API key — both are standard, low-cost, and readily available. Python with pandas, fuzzywuzzy, and a geocoding library covers the full pipeline without exotic dependencies.
Error Cost
Low
The output is a CSV used for analysis, not a live operational system. Errors are visible on inspection and easily corrected before the file is used downstream. No irreversible actions are taken.
Human Judgment Required
Low
Aside from confirming the deduplication definition upfront, there is no taste, ethics, or relationship context needed. Ambiguous city abbreviations can be flagged for human review rather than silently guessed.