Repeatability
High
The transformation logic is structurally identical for every row: derive segment from Q3-Q5, average Q12-Q18, extract keywords from open-ended fields. This is a deterministic pipeline that can be scripted and rerun on any future survey export with minimal changes.
Ambiguity Tolerance
Medium
The numeric columns and respondent_id are fully specified, but 'segment derived from Q3-Q5' and 'extracted sentiment keywords' leave room for interpretation — the segmentation logic and keyword extraction method need to be defined or inferred. A human should confirm the segmentation rules before the agent runs.
Data & Tool Availability
High
The agent needs only the raw CSV export, which the user already has. Standard Python libraries (pandas, nltk, or a lightweight LLM call) are sufficient to execute every step without external API dependencies or special permissions.
Error Cost
Low
The output is a CSV for pivot analysis — a human will review it before drawing conclusions. Mistakes are visible and reversible; no irreversible action or financial/legal consequence follows directly from a flawed transformation.
Human Judgment Required
Low
Arithmetic averaging and keyword extraction are mechanical. The only judgment call is how to define segment buckets from Q3-Q5, which a human should specify upfront but doesn't need to be involved in execution once defined.