Repeatability
High
The structure is consistent: ingest files, normalize column names, deduplicate, output a master and mapping file. This pattern repeats identically if run again with updated regional files, making it highly automatable.
Ambiguity Tolerance
Medium
The final deliverables are clearly defined (master file, conflict notes, mapping file), but the rules for resolving conflicts between duplicate records — which region's data wins, how to handle partial matches — are not fully specified and require either upfront rules or human sign-off on flagged cases.
Data & Tool Availability
High
The agent needs only the three Excel files and a Python/pandas environment with fuzzy matching libraries (e.g., rapidfuzz). No external APIs, live systems, or special permissions are required — everything is self-contained.
Error Cost
Low
The output is a new file, not a write-back to a live system, so mistakes are fully reversible. A human reviewer can audit the conflict notes and mapping file before the master is adopted, catching any bad merges.
Human Judgment Required
Medium
Most column normalization and exact deduplication is mechanical, but the ~45 fuzzy duplicates may include ambiguous cases (e.g., 'Acme Corp' vs. 'Acme Corporation Ltd') where a procurement expert should confirm the merge rather than trusting a similarity score alone.