AI & Technical question
A frontier lab says its finance agent aces isolated spreadsheet manipulations but fails on end-to-end FP&A workflows with exceptions, missing context, and ambiguous instructions. How would you diagnose whether the gap is in task decomposition, environment fidelity, training-data coverage, reward design, or evaluation, and turn that diagnosis into a prioritized roadmap?
- Scale AI
- AI & Technical
- Hard
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What this question tests
Whether you can diagnose an agent capability gap across five plausible root causes with real rigor, and turn the diagnosis into a prioritized roadmap rather than guessing at the most likely single cause.
How to approach it
- Task decomposition: check if the agent gets the end-to-end task as one instruction or must self-decompose; a gap here shows the agent attempting everything at once and losing track partway through.
- Environment fidelity: check whether training environments included exceptions and ambiguous instructions at all, since a model trained on clean tasks will predictably fail on messy real workflows.
- Training-data coverage: check whether labeled examples exist for handling ambiguity gracefully, like asking a clarifying question, versus only clean task completion.
- Reward design: check whether training rewards confidently wrong answers on ambiguous inputs the same as correct answers on clean ones, since that encourages guessing through ambiguity.
- Evaluation: check whether the eval suite tests messy end-to-end workflows or only isolated clean tasks, since a diagnosis is unreliable if the eval never exercised the failure mode.
- Prioritize fixing the weakest link first, likely environment fidelity and eval coverage, since those gate whether any other fix can even be verified.
What a strong answer includes
- Systematically checks all five candidate causes with a specific diagnostic signal for each, rather than guessing at the most likely one intuitively.
- Recognizes that eval coverage itself may be the root blind spot, since you cannot diagnose a failure mode your evaluation never tested for.
- Prioritizes environment fidelity and eval coverage as the gating fixes, since improving training data or reward design is wasted effort if the environment or eval cannot represent the real failure mode.
Common mistakes
- Picks one likely cause, like reward design, without a diagnostic step to confirm it over the other four candidates.
- Never considers that the evaluation itself might be blind to the exact failure mode being investigated.
Likely follow-up questions
- How would you build a diagnostic eval specifically for exception-handling behavior.
- What would you do if environment fidelity and reward design both need fixing simultaneously.
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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