Product design question
Assume Lovable's agent often fails mid-task because of bad reasoning, wrong tool calls, or incomplete multi-step execution. How would you identify the moments that most damage user trust, and what product changes would you prioritize so the agent recovers clearly instead of silently failing or misleading the user?
- Lovable
- Product design
- Hard
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What this question tests
Whether you can prioritize trust-damaging failure moments in an agentic product and design for graceful recovery instead of just reducing raw error rate.
How to approach it
- Identify trust-damaging moments specifically: silent failures where the agent claims success but the output is wrong, since those erode trust faster than an obvious visible error.
- Rank failure types by how much they mislead the user, not just by frequency: a confidently wrong claim is worse than an honest 'I could not complete this'.
- Prioritize product changes that make failure visible first, for example surfacing what step failed and why, before investing in reducing the underlying failure rate.
- Design a recovery pattern: when the agent detects a failed tool call or incomplete step, it should state what it attempted, what failed, and offer a clear next action (retry, rollback, or manual edit).
- Add a checkpoint or diff view so users can see what changed before and after a multi-step edit, reducing the cost of an undetected bad change.
- Measure success by reduction in silent-failure incidents and by user trust signals (task completion after a recoverable error) rather than raw error count alone.
What a strong answer includes
- Names silent, confidently-wrong failures as the highest-priority category, since they are the ones that actively mislead rather than just disappoint.
- Proposes a concrete UX pattern (explicit failure statement plus rollback or retry option) rather than a vague 'improve error handling'.
- Distinguishes 'reduce failure rate' from 'improve recovery from failure', arguing the second matters more for trust in the near term.
- Ties prioritization to user-trust metrics, for example task retry-then-success rate after an error, not just error frequency.
Common mistakes
- Treating all agent failures as equally damaging instead of ranking by how misleading they are.
- Only proposing to reduce the failure rate without addressing what happens when failure occurs.
- No concrete UX mechanism for surfacing what went wrong.
Likely follow-up questions
- How would you detect a silent failure automatically rather than relying on user reports?
- What would you do if adding failure visibility increases user-perceived friction?
- How would you measure whether recovery design actually restored trust?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop