Product Design · take-home exercise · 7-day sprint
End-to-end design for an AI agent in the Shopify ecosystem — scan, diagnose and autonomously repair storefront issues, with configurable trust modes and a full audit trail.
[ 01 · The brief ]
Autonomy is easy to build and hard to trust.
The brief was an agent that could find and fix storefront problems by itself. The design problem was not detection — it was consent. A merchant will not hand write-access to their live store to something they cannot supervise.
So the product had to make autonomy adjustable, and every action reversible and inspectable.
[ 02 · Process ]
What I did
- Mapped the merchant mental model around risk, revenue and reversibility
- Designed three trust modes: suggest only, approve each fix, and autonomous within limits
- Built a diagnostic surface that ranks issues by revenue impact, not severity score
- Designed a full audit log so every autonomous action is attributable and undoable
“The unit of trust is not accuracy. It is reversibility.”
Design principle, ShopOS
[ 03 · Findings ]
The design position
Rather than asking merchants to trust the agent globally, the product lets them grant autonomy per category and per risk level, then widen it as the track record builds.
Every fix ships with a before and after, a plain-language reason, and a one-click revert. Trust is earned incrementally and visibly.
[ 04 · Outcome ]
What changed
The exercise produced a complete product design: positioning, diagnostic flow, agent configuration, audit trail and a component system. The core move was reframing an accuracy problem as a permissions problem.
Agent products fail on governance long before they fail on capability. Designing the leash is the design work.
Reflection