Product design question
How would you design Ramp's AI agents (Policy, AP, Accounting) to be trusted with company money?
- Ramp
- Product design
- Hard
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What this question tests
Tests product design for AI agents operating with real financial authority, where trust and control are the central design problem.
How to approach it
- Define the risk precisely: agents like Policy, AP, and Accounting can approve spend, process payments, or categorize transactions, so a mistake has direct financial consequences, not just inconvenience.
- Design graduated autonomy: start new agent deployments in a recommend and confirm mode, and expand to autonomous action only after a proven track record on that specific customer's data.
- Design clear scope boundaries: each agent should have explicit limits, like a maximum transaction size it can approve autonomously, beyond which it must escalate to a human.
- Build full auditability: every agent decision logged with the reasoning and data it used, so finance teams can review and explain any action after the fact.
- Add anomaly detection as a safety net: flag agent decisions that deviate from historical patterns for human review, even within otherwise authorized limits.
What a strong answer includes
- Proposes graduated autonomy tied to a proven track record rather than granting full autonomous authority from day one, directly addressing the core trust barrier with financial agents.
- Sets explicit, scoped limits like maximum autonomous transaction size, giving finance teams a concrete, adjustable control lever rather than an all or nothing trust decision.
- Builds full auditability as a core requirement, recognizing finance teams need to explain and defend every automated decision, not just trust it worked.
Common mistakes
- Proposing full autonomous authority from the start without a track record or scoped limits, ignoring the real trust barrier with financial actions.
- No audit trail, leaving finance teams unable to review or explain an agent's decision after the fact.
Likely follow-up questions
- How would you decide when an agent has earned expanded autonomous authority?
- What would you do if anomaly detection had a high false positive rate, causing alert fatigue?
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More questions from Ramp
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