Parallelizing a checkout flow is exactly the kind of structured refactor AI handles well.

The task follows well-established patterns — Promise.all for parallelization, optimistic UI hooks, deduplication via request IDs, Sentry instrumentation — that AI agents have seen thousands of times. The structure is consistent and not context-dependent in unusual ways.

Success criteria are mostly crisp (4s → faster, Sentry at each step, deduplication on retry), but which API calls are truly independent requires understanding business logic — e.g., can tax calculation run before coupon is applied? That dependency graph needs to be inferred or clarified.

The agent needs full access to the existing codebase, API contracts, and a Sentry DSN. If given repo access and environment config, it has what it needs — but without the actual code, it can only produce a template, not a working implementation.

This touches a payment flow. A race condition, incorrect parallelization of inventory reservation and payment, or broken retry deduplication could cause double charges, overselling, or silent failures. Code review before deployment is non-negotiable.

Determining the safe parallelization order (e.g., fraud check must precede payment) requires business logic understanding that may not be obvious from code alone. A developer should validate the dependency graph the agent produces before merging.