Repeatability
High
The transformation logic — deduplication rules, stage label mapping, days-in-pipeline calculation — is structurally identical every time this export is run. This is a repeatable ETL pattern with no instance-specific judgment required.
Ambiguity Tolerance
Medium
Exact duplicate removal and days-in-pipeline are fully crisp. The 'likely duplicate' flagging rule (same name + similar email domain) is underspecified — the agent needs a defined similarity threshold — but the task wisely routes those to human review rather than auto-resolving them.
Data & Tool Availability
High
Both files are static exports the user already has; no live API access or authentication is needed. The agent just needs the two files and a stage-label mapping table, which can be inferred or provided by the user.
Error Cost
Low
The output is a CSV for review, not a live system write. Errors are visible and reversible — a human can spot-check the flagged duplicates and re-run if the logic was wrong. No candidate data is deleted from source systems.
Human Judgment Required
Low
The task explicitly offloads the one genuinely ambiguous decision — likely duplicates — to human review. Everything else is deterministic transformation logic that requires no intuition or relationship context.