risk

Trust that outruns accuracy is as dangerous as distrust. The interviewer wants design that keeps experts engaged where it matters, without making the tool tedious.

Regulated industries can move fast with AI, but not the way consumer apps do. The interviewer wants a strategy that sequences use cases by risk and turns governance into speed.

Money movement is where agent design stops being theoretical. The strong answer never lets the model be the control, and shows exactly where policy, approval and idempotency sit.

Accuracy alone rewards guessing. Set a penalty for wrong answers from what errors cost in your product, score both models under it, and the choice usually flips.

IP risk in generative products comes from four directions: what the model was trained on, what users put in, what comes out, and what the contracts say. The interviewer wants all four and practical controls, without pretending the law is settled.

A person behind the curtain answers whether users want it; a real model answers whether it works. Pick by the uncertainty you need to resolve, and never fake it with paying users without telling them.

The test is choosing what to build, not building fast. Name the riskiest assumption in your own case, build only the slice that tests it and say what result would change your recommendation.

Most decisions should be fast; a few should be slow. Show that you classified the decision first, and that you turned one irreversible step into several reversible ones where you could.