reliability

A small-sounding problem that becomes a big one at volume. The good answer explains why free-form generation produces malformed output and which controls make it near-impossible.

Every AI product depends on services it does not control. The interviewer wants a degradation plan by feature, written and tested before the outage starts.

Launches fail on rate limits more often than on model quality. This question checks whether you can turn expected usage into tokens per minute, compare it with limits, and plan headroom.

The compounding arithmetic every agent PM should be able to do in their head. Once you show 0.95 to the tenth power, the interviewer wants to know which of four levers you would pull first.

A runaway agent is a cost problem, a reliability problem and a product problem at once. The answer is part diagnosis, part guardrails, and part design for what happens when a budget runs out.

Multi-agent diagrams look sophisticated in a design review. The interviewer is checking whether you can name the specific conditions that justify them and the costs they add.

Browser agents can reach any website on day one, which makes them irresistible in a demo. The question is what they cost in reliability, speed and legal exposure once real users depend on them.

A timeout does not tell you whether money moved. Define the recovery states, retry contract and customer messages that prevent a helpful agent from issuing the same refund twice.

An approval can arrive after the worker has restarted. Choose a runtime from that requirement, show its recovery behavior and avoid repeating a purchase after a timeout.

A canceled chat stream leaves work running in three services. Define the user promise, reconcile actions already sent and test the race at the execution boundary.

The headline horizon is a 50% success rate on someone else's tasks. Plan on the 80% horizon for your own tasks, break long jobs into checkpoints and keep long-autonomy features as dated bets.

AI incidents often have no outage, no error and no deploy: just a model saying or doing something harmful at scale. The plan has to detect those, contain them fast, and learn from them.

A platform metrics question. Token volume is the obvious answer and the incomplete one. The interviewer wants the developer funnel, reliability, economics and migration health in one coherent set.

Model latency depends on input length, output length, load and the model itself, so a naive guarantee is either meaningless or impossible. The interviewer wants a guarantee that is defined precisely, measurable and backed by capacity.