Repeatability
High
The transformation logic is identical every run: fixed date arithmetic, hard-coded grade thresholds, and quartile-based bucketing. There is no instance-specific judgment; the same script handles any batch of records with the same schema.
Ambiguity Tolerance
High
Success criteria are fully specified: exact grade cutoffs, three engagement tiers derived from watch-time quartiles, deduplication on a defined key, and flagging of missing critical fields. A non-human can verify correctness mechanically against these rules.
Data & Tool Availability
High
The agent needs only the exported JSON file and a Python or data-processing environment — both are standard and readily available. No external APIs, credentials, or live system access are required.
Error Cost
Low
The source JSON is untouched, so any output error is fully reversible by re-running the transformation. The worst realistic outcome is a downstream report built on a miscalculated column, which is easily caught on review.
Human Judgment Required
Low
Every decision rule is explicitly defined by the user. The only edge case worth noting — how to handle ties at quartile boundaries — is a minor implementation choice the agent can resolve with a standard convention and document in output notes.