Product design question
You are onboarding a large Italian enterprise with multiple support queues and limited integration capacity for v1. How would you discover their support requirements, define the initial agent scope, and set explicit rules for which intents the agent should resolve end-to-end versus escalate to a human?
- Sierra
- Product design
- Medium
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests requirements discovery and scoping under a real constraint, limited integration capacity, and the ability to set clear automation versus escalation rules.
How to approach it
- Map the multiple support queues first to find which have the highest volume and the simplest, most well documented resolution steps.
- Assess integration capacity honestly against what each queue would require, prioritizing queues that need little or no new integration work for v1.
- Define the initial agent scope around one or two queues that combine high volume with low integration cost.
- Set explicit rules for full resolution versus escalation per intent within scope, based on complexity and risk, not a blanket policy.
- Confirm the scope and escalation rules with the customer's support leadership before build, since they know which queues are truly safe to automate.
What a strong answer includes
- Uses integration capacity as an explicit filter for queue selection, matching the real constraint stated in the question rather than ignoring it.
- Sets escalation rules per intent rather than one blanket rule, since risk and complexity vary even within a single queue.
- Validates scope with the customer's own support leadership, who has the ground truth on which cases are safe to fully automate.
Common mistakes
- Choosing queues to automate based on customer preference alone without weighing integration cost against the limited v1 capacity.
- Setting one blanket escalation rule across all queues instead of intent level rules matched to actual risk.
Likely follow-up questions
- How would you decide which queue to add next once integration capacity frees up?
- What would you do if the highest volume queue also has the highest integration cost?
More product design questions
- How would you improve Sierra's AI agents to resolve more customer issues without escalation?Sierra · Product design · Medium
- Design an agent that works seamlessly across chat, voice, email, and SMS.Sierra · Product design · Hard
- If you joined Sierra today, what developer-facing platform capabilities would you put in v1 to help engineers deploy, observe, and iterate on AI agents reliably? Be specific about the first APIs, SDKs, workflows, or internal tools you would build, who they serve, and what criteria would determine whether something belongs in the initial platform versus a later release.Sierra · Product design · Medium
- A large healthcare payer wants Sierra to launch a customer-support agent in 8 weeks. How would you discover requirements, choose the first workflows to automate, and define an MVP that is safe enough for launch but still delivers measurable value?Sierra · Product design · Hard
- Sierra is launching the first version of its Agent SDK for enterprise developers. What would you include in the v1 launch scope, and how would you prioritize among core integration primitives, customization hooks, observability, safety controls, and brand configuration? Be explicit about the tradeoffs you would make to balance fast time-to-value with enterprise requirements.Sierra · Product design · Hard
- Sierra is considering a new SDK capability to help companies create more brand-aligned, human-sounding agents. How would you identify the right opportunity, validate that customers will use it, and decide whether it is worth investing in as a 0→1 product bet?Sierra · Product design · Hard
More questions from Sierra
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop