agents

Users let an assistant act when they can see what it did and take it back. The interviewer wants an activity log, real undo, and clear handling of actions that cannot be undone.

Approval screens fail in a predictable way: people click 'approve all'. The interviewer wants a design that focuses attention on the changes that matter and makes careful review fast.

Delegation is the highest autonomy level and the hardest to design. Principal answers cover the brief, budgets and boundaries, checkpoints, reporting, and what happens when the agent hits something it should not decide.

The innovator's dilemma in SaaS form. If you don't ship the AI, competitors will; if you do, per-seat revenue shrinks. The interviewer wants a pricing and product transition that grows revenue with the work done.

Explain what the model sees on each call, why more context can make answers worse and costlier, and which parts of the context recipe a PM owns.

Choose the least autonomy that does the job, explain exactly which step deserves a model, and know the one place in onboarding where an agent may genuinely earn its keep.

A definitions question that comes up early in agent rounds. A precise answer here sets up every design answer that follows.

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.

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.

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.

A product design question where the hard part is not the model. It is deciding which actions the agent takes alone, which it drafts, and how the user learns to trust it.

A vocabulary question that turns into a strategy question. The interviewer wants to know whether you see MCP as plumbing or as a distribution decision.

Memory makes assistants feel personal and creates a new way to be wrong, creepy or unsafe. The strongest answers start with what should never be remembered.

Handoffs decide how customers remember an AI support experience. The interviewer wants explicit triggers, a view on the two kinds of error, and a handoff the human can act on without re-asking.

Agent output is cheap; human review is not. This question tests whether you measure the agent by what it costs the team to accept its work, not by how much work it produces.

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 range is not a pricing input. Work out cost per accepted task by effort level and cost per account by cohort, compare flat, allowance and usage plans on the same customers, then say what would change your choice.

A stakeholder question wrapped in an agent question. The strong answer protects the deal and the customer by turning a capability promise into an outcome commitment with a path.

A principal-level design question with money, regulation, fraud and a dozen languages in one queue. The answer that scores separates complaint types by what the agent may do, and keeps every money decision in deterministic systems.

A platform decision with a three-year shadow. Hosted platforms ship faster today; owning the stack keeps options open. Principal answers decide which layers must stay portable and accept convenience everywhere else.

Approval queues create the best dataset an agent team will ever have. This question tests whether you use it to promote autonomy action by action, with a bar set before you look.

A fundamentals question with a trap: the model never touches your systems. Getting that right is what lets a PM reason about safety and control later.

An incident question where the root cause is almost never 'the model made a mistake'. The interviewer wants containment first, honest communication, and a fix to the permission design that made deletion possible.

Research agents are judged on whether an analyst can trust and reuse the output. The design centres on source quality, verifiable citations and showing the plan before spending ten minutes.

Security teams are usually right about agents, and the PM who treats them as an obstacle loses. Principal answers turn the CISO's concerns into requirements, then design a rollout the security team can sign.

Two problems hide in one complaint: the agent is slow, and waiting for it feels worse than it needs to. Strong answers attack both, and question whether every task should make the user wait at all.

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.

Every connected tool adds a description the model must read on every call. Diagnose tool confusion from traces, then cut, rename and load tools by task.

Agent evals are where answers that worked for chatbots stop working. A correct final booking can hide a path that broke policy, spent money twice or took nine minutes.

Metric trees for AI agents need two layers classic product trees do not have: quality and risk. This question checks whether you include them and connect them to the business outcome.

Agent prompts tempt candidates into ambitious builds that collapse. The strong answer builds the least autonomous version that demonstrates the idea, with mocked tools, clear limits and tests of the hard paths.

Companies that shipped MCP servers exposed reads widely and held back money-moving actions. Start with a few consolidated read tools, test descriptions against real tasks, and gate anything irreversible.