Product design question
How would you improve ElevenLabs' Agents platform for voice customer support?
- ElevenLabs
- Product design
- Medium
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What this question tests
Tests product design for a voice agent platform serving developers who build customer support experiences on top of it.
How to approach it
- Define the user: a developer or CX team building a voice support agent on ElevenLabs Agents, not the end customer talking to it.
- Identify the core pain points today: tuning conversational flow, handling interruptions naturally, and debugging why a call went wrong after the fact.
- Prioritize the biggest lever: better tooling to design and test conversation flows before they go live, since voice agents are hard to debug purely through code.
- Design a conversation testing environment: simulate common call scenarios, including interruptions and off script questions, before deploying to real customers.
- Add observability: call transcripts with sentiment and outcome tagging, so a team can see which calls failed and why without listening to every recording.
- Confirm with the interviewer whether the priority is developer experience building the agent, or end customer experience during the call.
What a strong answer includes
- Prioritizes a pre launch testing environment for conversation flows, addressing the real pain of voice agents behaving unpredictably once live.
- Proposes structured call analytics with outcome tagging, letting teams find failure patterns without manually reviewing every recorded call.
- Addresses interruption handling explicitly, since natural turn taking is one of the hardest and most noticeable gaps between good and bad voice agents.
- Ties improvements to a measurable outcome, like reduced call escalation rate to a human agent, rather than only describing developer convenience.
Common mistakes
- Focusing only on voice quality or latency while ignoring the harder problem of conversation design and debugging.
- No observability or analytics proposal, leaving teams unable to diagnose why calls are failing in production.
- Designing for the developer's convenience without tying it back to end customer call outcomes.
Likely follow-up questions
- How would you help a team debug a specific call that went badly?
- What would you measure to know the platform improvement actually worked?
- How would you handle a caller who repeatedly interrupts the agent?
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More questions from ElevenLabs
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