AI & Technical question

How would you measure the quality of code produced with Windsurf's agent?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

AI and technical metrics for code quality produced by an agentic coding assistant, extending beyond simple pass/fail checks.

Define quality across layers: functional correctness (tests pass, no regressions), code health (follows existing style and architecture), and reviewer trust (how much rework a human needs).
Track automated signals: percentage of Cascade-generated changes that pass the project's existing CI pipeline without modification.
Track human-review signals: lines changed by a human reviewer after the agent's output, as a proxy for how close to done the work was.
Track downstream signals: bug and rollback rate specifically tied to agent-authored changes over the following weeks.
Combine into a composite quality score tracked over time to evaluate whether model or prompting changes improve real output quality.
Segment by task type, since a simple refactor and a multi-file feature addition have very different baseline quality expectations.

Defines quality with concrete, checkable criteria across automated, human, and downstream layers.
Uses post-generation human edit volume as a practical, hard-to-game proxy for closeness to production-ready.
Adds downstream bug and rollback tracking to catch issues that pass initial review but surface later.
Segments by task complexity to avoid a misleading blended score.
Frames the composite score as a feedback tool for improving Cascade over time, not just a static report.

Using CI pass rate alone as the quality bar, ignoring maintainability and reviewer trust.
Not segmenting by task complexity, which skews the aggregate quality number.

Chapters of the AI PM course that teach what this question tests.