Repeatability
High
The structure is identical every time: read CSV, normalize string variants to canonical channel names, aggregate metrics, output JSON. This pattern generalizes cleanly across runs and datasets.
Ambiguity Tolerance
High
Success criteria are concrete — deduplicated utm_source values, consistent casing, and aggregated metrics in JSON. The only mild ambiguity is deciding canonical names for edge cases, which can be resolved with a simple mapping table the user provides or the agent proposes for approval.
Data & Tool Availability
High
The user has the CSV ready and the output format is specified. A code-capable agent needs only file access and a Python or pandas environment — no external APIs or credentials required.
Error Cost
Low
The source data is never modified, so mistakes are fully reversible. Output errors are easy to spot by spot-checking row counts or channel totals against the raw data before the JSON is pushed to any dashboard.
Human Judgment Required
Low
Fuzzy string matching and canonical name resolution are well within current AI capability. The only human input needed is a quick review of the proposed channel mapping before finalizing — not active judgment during execution.