Product design question
Design Ghostwriter’s first-run experience from prompt to live agent. How would you help a new CX team go from a plain-English goal to a deployed agent quickly, while creating a clear path for advanced teams to inspect and control journeys, integrations, simulations, and approvals before launch?
- Sierra
- Product design
- Hard
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 designing an onboarding flow that gets a non-technical team to a working AI agent fast while giving advanced teams real control before launch.
How to approach it
- Design the first-run path as prompt-to-draft-agent in minutes, using sensible defaults for journeys, integrations, and routing.
- Immediately show the draft agent's behavior via a few sample simulated conversations so the team can judge quality before going further.
- Build a clear inspect-and-edit layer beneath the quick-start default, letting advanced teams open journeys and routing without starting over.
- Require a simulation and approval step before any agent goes live, regardless of how quickly it was built.
- Let advanced teams save and reuse custom configurations as templates for future agents.
What a strong answer includes
- Keeps the fast path genuinely fast, such as a live draft agent within minutes of a plain-English prompt.
- Makes advanced configuration additive rather than a fork, so an advanced team isn't forced into a separate, harder tool.
- Uses simulated conversations as the trust-building step immediately after the draft, not buried later in the flow.
- Keeps the approval gate before live traffic non-negotiable for both novice and advanced paths.
Common mistakes
- Building two disconnected experiences, one simple and one advanced, that don't share the same underlying agent.
- Letting the fast path skip simulation and approval in the name of speed.
Likely follow-up questions
- How would you handle a new team that wants to skip the simulation step to launch faster?
- What would you show an advanced team in week one to convince them to go beyond the defaults?
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