Streaming CSV refactors are exactly the kind of bounded coding task AI handles well.

This is a structurally standard refactoring pattern — replace eager loading with streaming, add incremental validation, write error rows to a side channel. The pattern repeats reliably across codebases and is well-represented in training data.

Core success criteria are crisp (no OOM, backward-compatible API, error report output), but specifics like validation rules, error report format, and acceptable memory ceiling are not defined and will require inference or assumptions from the agent.

The agent needs the existing service code, current validation logic, API contracts, and ideally test fixtures with large CSVs. If the codebase is provided in full, this is tractable; if the agent must guess at existing structure, quality degrades.

A bad refactor could silently drop rows, corrupt imports, or break the upload API for all users — real production risk. However, the change is reversible via version control and should be caught in code review and testing before deployment.

The engineering decisions here — using IAsyncEnumerable or CsvHelper streaming, batching DB writes, structuring the error report — are well-established patterns with clear tradeoffs that don't require intuition or organizational context.