AI & Technical question
A major enterprise customer says its agent feels on-brand in straightforward conversations but inconsistent in higher-stakes ones. How would you determine whether the root cause is data quality, prompt or tooling issues, or model behavior, and how would you translate that diagnosis into a concrete plan for engineering and customer-facing teams?
- Sierra
- AI & Technical
- Hard
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What this question tests
Root-cause diagnosis for an AI agent quality issue: can you separate data, prompting/tooling, and model behavior as distinct causes and translate the diagnosis into an actionable plan for two different audiences.
How to approach it
- Reproduce the inconsistency with real high-stakes transcripts, tagging exactly where the agent goes off-brand, tone, factual accuracy, or escalation handling.
- Check data quality first: is the knowledge base thin or outdated on high-stakes topics, since agents often default to generic behavior when grounding data is missing.
- Check prompting and tooling next: does the agent have the right tools and guardrail prompts specifically for edge cases, or only for the common path.
- Only after ruling out data and tooling, treat it as model behavior, for example a base model tendency to hedge or drift under ambiguous instructions.
- Translate the diagnosis into two plans: an engineering plan (data or tooling fixes, prompt or eval changes) and a customer-facing plan (what changed, what to monitor, and interim safeguards for high-stakes conversations).
What a strong answer includes
- Orders the diagnosis data, then prompting and tooling, then model, since that reflects where problems most commonly and cheaply originate.
- Uses real high-stakes transcripts to localize the failure instead of speculating about the cause.
- Produces two distinct deliverables, an engineering fix plan and a customer-facing plan, matching what the question asks for.
Common mistakes
- Jumps to blaming the model without first ruling out data and tooling gaps.
- Gives only a technical plan with nothing for the customer-facing team to act on.
Likely follow-up questions
- What interim safeguard would you put in place for high-stakes conversations while the fix ships.
- How would you confirm the fix actually generalizes beyond the transcripts you reviewed.
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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