AI & Technical question
During a peak support window, a live Sierra agent starts giving incorrect answers across many conversations. How would you contain the issue, decide whether to narrow or disable automation, inspect whether the failure comes from prompts, retrieval, tool calls, or upstream data, and define the permanent fix?
- Sierra
- AI & Technical
- Hard
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What this question tests
Tests incident response under pressure: containing a live quality failure, diagnosing root cause across the stack, and defining a permanent fix, not just stopping the bleeding.
How to approach it
- Contain immediately: narrow or disable automation for the affected conversation type or customer segment first, accepting a temporary capacity hit to stop incorrect answers from reaching more customers.
- Triage severity: check whether the incorrect answers are isolated to one topic or intent, or spread broadly across many conversation types, which changes how aggressively to narrow automation.
- Inspect the stack in order: check for a recent prompt or configuration change first since it's the most common and fastest-to-verify cause, then retrieval or knowledge-base freshness, then tool-call behavior, then upstream data source changes.
- Once the specific cause is found, for example a stale knowledge base after an upstream data update, fix that root layer directly rather than patching downstream symptoms.
- Re-enable automation gradually, starting with a limited traffic percentage, monitoring closely for a recurrence before returning to full volume.
- Define the permanent fix: if the cause was upstream data changing silently, add monitoring or a validation check on that data source so this failure mode is caught automatically next time, not just this once.
What a strong answer includes
- Contains first with a deliberate, even costly, narrowing of automation, prioritizing stopping harm over avoiding capacity loss.
- Inspects the stack in a fast-to-slow order, recent changes first, since that's the most common root cause and fastest to verify.
- Re-enables gradually with monitoring rather than flipping straight back to full automation once a fix is found.
- Proposes a permanent structural fix, like source-data monitoring, that prevents recurrence, not just a patch for this one incident.
Common mistakes
- Trying to diagnose the root cause before containing the live harm, letting bad answers keep reaching customers.
- Re-enabling full automation immediately after a fix without a gradual, monitored ramp-up.
- Fixing only the immediate symptom without building a structural safeguard against recurrence.
Likely follow-up questions
- How would you decide how aggressively to narrow automation during containment?
- What monitoring would you add to catch this class of failure automatically next time?
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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