AI & Technical question
An enterprise customer wants a highly customized agent launched this quarter, but engineering believes the customer’s data quality is poor and the evaluation set is too weak to support a reliable release. How would you assess the risk, align on launch criteria, and handle the conversation with both the customer and the internal team if they disagree?
- Scale AI
- AI & Technical
- Hard
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What this question tests
Tests assessing launch risk when engineering and the customer disagree about data quality and eval robustness, and managing the resulting conversation on both sides without avoiding the disagreement.
How to approach it
- Get specific about the disagreement: what exactly does engineering believe is weak, data coverage, label quality, or eval set size, and quantify the gap against Scale's normal launch bar.
- Assess the real risk of launching anyway: what's the worst plausible failure mode for this specific customized agent, and how reversible or contained is it if it occurs.
- Propose an objective resolution mechanism, for example an expanded eval set built jointly with engineering's concerns addressed, with a defined pass bar, rather than a subjective launch or don't launch debate.
- If the customer wants to launch faster than the evidence supports, propose a scoped, monitored limited launch that reduces exposure, rather than a binary full launch or full delay.
- Handle the internal conversation by validating engineering's concern is heard and addressed in the launch criteria, not overridden by customer pressure.
- Handle the customer conversation by being transparent about the specific risk and the scoped mitigation plan, rather than either hiding the concern or blocking without offering an alternative path.
What a strong answer includes
- Quantifies the specific gap engineering is concerned about instead of treating disagreement as a vague vibes-based standoff.
- Proposes an objective resolution, an expanded eval set with a defined pass bar, that both sides can evaluate against rather than relying on authority.
- Offers a scoped, monitored limited launch as a middle path, avoiding a false binary between full launch and full delay.
- Handles both conversations with transparency, showing engineering their concern shaped the criteria, and showing the customer the real risk and mitigation, not a spin.
Common mistakes
- Letting customer urgency override a legitimate technical concern without addressing it directly.
- Treating the disagreement as unresolvable and picking a side rather than proposing an objective test.
- Hiding the actual risk from the customer instead of offering a transparent, scoped alternative.
Likely follow-up questions
- What would you do if the expanded eval set still shows borderline results?
- How would you decide the scope of a limited launch in this situation?
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More questions from Scale AI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture