Repeatability
High
The transformation logic — schema mapping, fuzzy matching, missing-field flagging — is structurally identical every run. This is a deterministic ETL pipeline, not a judgment-heavy task.
Ambiguity Tolerance
Medium
The output format and flag criteria are clearly defined, but the fuzzy-match threshold for 'duplicate' company names is inherently ambiguous — 'Acme Corp' vs 'Acme Corporation' is easy, but 'Global Partners LLC' vs 'Global Partners Inc.' requires a judgment call that may not always be correct.
Data & Tool Availability
High
Airtable has a well-documented REST API, and all three bases are described as accessible. An agent with API credentials and a Python/scripting environment has everything it needs to extract, transform, and output the data.
Error Cost
Medium
A bad fuzzy merge could silently collapse two distinct clients into one record, corrupting billing history — but the output is a CSV, not a live database write, so errors are reviewable and reversible before any downstream action is taken.
Human Judgment Required
Low
The task is mechanical: map columns, match strings, flag nulls. No relationship context, ethical judgment, or subjective taste is needed. A human should review the dedup log, but the agent can produce it without human input.