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Published Jun 6, 2026
7 min read
An Auditable AI Data Analysis Agent From CSV Question to Conclusion
Reduce automated-analysis risk with data dictionaries, query plans, code execution, validation, and evidence links.
Key takeawayAn analysis agent must trace back to data, queries, and computation—not only produce a fluent explanation.
Why this deserves its own decision
Letting a model read a table and answer is fast, but ambiguous fields, missing values, time windows, and aggregation can make a syntactically correct result wrong for the business. High-impact decisions cannot rely on a prose summary alone.
Decision framework
- Provide a data dictionary, permissions, and allowed query scope.
- Show the query and computation plan before executing code or SQL.
- Link conclusions to result tables, code, and input versions.
Putting it into a ModelRush workflow
Use separate ModelRush routes for planning, code generation, and result review. A sandbox executes queries and retains logs; a second review route checks units, filters, and anomalies. Final answers include conclusion, limitations, and reproducible evidence.
What to measure after launch
- Query execution, result reproduction, and reviewer findings.
- Errors from field meaning, filters, or aggregation.
- Analyst time required to correct agent conclusions.
An analysis agent must trace back to data, queries, and computation—not only produce a fluent explanation.
