Repeatability
High
The task is structurally identical every time: read JSON, group by defined dimensions, compute aggregates, write CSV. No judgment varies between runs — only the data changes.
Ambiguity Tolerance
High
The three output tables are explicitly named, the anomaly types are specified, and the file format is defined. There is minimal room for misinterpretation about what 'done' looks like.
Data & Tool Availability
High
The agent needs only the 2.3 GB JSON file and a Python environment with pandas or similar — both are standard and accessible. No external APIs or credentials are required.
Error Cost
Low
The output is a CSV for analysis, not a decision or an irreversible action. Any errors in aggregation are visible on inspection and trivially correctable by re-running the script.
Human Judgment Required
Low
Defining 'impossible timestamps' and 'null user IDs' is objective and rule-based. The aggregation logic requires no taste, ethics, or contextual intuition — just correct implementation.