Good AI Task

AI compatibility

Cleaning and aggregating 4M usage events for Tableau is a textbook job for a data agent.

Good fit

AI can handle this.

Average across 1 submission.

88
avg / 100

The honest read

This is a well-scoped data transformation task with explicit, unambiguous rules: filter on a string pattern, aggregate on defined fields, calculate a standard metric, and flag outliers by a hard threshold. The success criteria are fully deterministic and the output format is specified. The only real risk is data access setup and handling edge cases in the raw JSON, both of which are manageable.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The transformation logic is fully specified and structurally identical every run — same fields, same filters, same aggregations. This is a repeatable ETL pipeline, not a judgment call.

Ambiguity Tolerance

High

Every rule is crisp: bot filter is a string match, session error threshold is a hard 12-hour cutoff, aggregation keys are named. There is no subjective interpretation required.

Data & Tool Availability

High

The agent needs read access to the JSON file and write access to an output location — both are standard and grantable. Python with pandas or polars handles 1.2 GB comfortably.

Error Cost

Low

The output is a CSV for analysis, not a write-back to production. A bad run produces a flawed report, not data loss or irreversible action — easy to rerun and verify.

Human Judgment Required

Low

All transformation rules are explicit and deterministic. No taste, ethics, or contextual business knowledge is needed to execute this correctly.

What an agent would need

  • Read access to the 1.2 GB JSON file (local path, S3 URI, or equivalent)
  • A Python or similar scripting environment with a dataframe library capable of handling the file size (e.g., pandas, polars, or DuckDB)
  • Write access to an output directory for the resulting CSV
  • Confirmation of the exact user_agent field name in the JSON schema to apply the bot filter correctly
  • Clarification on whether 'daily active users' should be a separate summary table or a column in the main aggregated output

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