AI & Technical question
A deployed Sierra agent resolves most conversations but fails on a small set of high-stakes cases. How would you determine whether to invest first in model changes, better retrieval/context, workflow constraints, or earlier human handoff?
- Sierra
- AI & Technical
- Hard
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What this question tests
Tests diagnosing where a deployed agent's failures concentrate across model, retrieval, workflow design, or escalation timing, and choosing the right investment instead of a default model upgrade.
How to approach it
- Pull the specific failing conversations and categorize each: did the model misunderstand intent, was the retrieved context wrong or missing, did the workflow lack a defined path for this case, or did escalation happen too late after the agent already made a mistake.
- Check if failures cluster around a specific topic or intent, which would point to retrieval or context gaps rather than general model capability.
- Check if failures involve genuinely novel or edge-case scenarios outside the defined workflow paths, which points to workflow constraints needing expansion, not a model problem.
- Check escalation timing specifically: did the agent recognize uncertainty but escalate too late, after already giving a wrong or risky answer, which is a workflow design fix, not a model fix.
- Rank the investment by which category explains the most high-stakes failures, since fixing the wrong layer, for example upgrading the model when the real gap is retrieval, wastes effort and delays the actual fix.
- Prioritize earlier human handoff as a near-term safety net for the highest-stakes failure category while the underlying fix, retrieval, workflow, or model, is being built.
What a strong answer includes
- Categorizes failures by specific cause before choosing an investment, avoiding a default model upgrade that may not address the real gap.
- Distinguishes a workflow-design gap, missing case coverage, from a genuine model-capability gap, which need entirely different fixes.
- Identifies escalation timing as its own fixable category, since recognizing uncertainty too late is a workflow issue, not a model quality issue.
- Proposes earlier human handoff as an immediate safety mitigation while the root-cause fix is still being built, protecting against ongoing high-stakes harm.
Common mistakes
- Defaulting to a model upgrade without categorizing whether failures are really retrieval, workflow, or escalation issues.
- Treating all high-stakes failures as one undifferentiated bucket instead of finding the dominant failure category.
- Delaying any safety mitigation, like earlier handoff, until the full root-cause fix is complete.
Likely follow-up questions
- How would you tell a retrieval failure from a genuine model reasoning failure?
- What threshold would trigger tightening the escalation rule further?
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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