Repeatability
High
The transformation logic is structurally consistent: parse JSON, resolve field location variants, normalize deliverables to a count, write CSV. Once the mapping rules are codified, the same script handles all 1,850 records identically.
Ambiguity Tolerance
High
The target schema is fully specified (seven named columns) and the known structural variants are described. Success is objectively measurable: 1,850 rows, correct columns, no nulls where data exists.
Data & Tool Availability
High
The agent needs only the JSON file and a Python or JavaScript runtime—both trivially available. No external APIs, credentials, or live systems are required to complete the transformation.
Error Cost
Low
The source JSON is read-only and the output is a CSV staged for import, not yet loaded into the live system. Errors are detectable before any data lands in production, and the original file is untouched.
Human Judgment Required
Low
Field mapping and normalization rules are mechanical once the schema is defined. The only judgment call—how to handle truly malformed records—can be handled by flagging them in a separate error log for human review rather than silently dropping or guessing.