Amazon Rufus

Why Rufus?

Amazon Rufus is an AI shopping assistant embedded in the Amazon app. It's a real product, actively used by millions, and it demonstrates exactly the kind of complexity this toolkit is designed to evaluate.This case study shows how to apply everything you've learned to a real-world agent.

Rufus is a multi-agent system. Behind one interface, multiple specialized capabilities coordinate: search, comparison, cart management, price tracking, and potentially auto-purchase. This makes it an ideal audit subject because multi-agent systems fail between agents, not within them.

It's also e-commerce, which means real money, real transactions, and real consequences when things go wrong.

This Audit Plan Uses:

Four packs combined into three targeted tasks.

Core Usability Pack
E-Commerce Pack
Multi-Agent Pack
Long-Context Pack

Challenge

Multi-agent coordination
Safety-critical actions
Long-context journeys
Post-purchase support

Why It Matters

Handoffs, routing, memory sharing
Auto-buy, payments, irreversible purchases
Multi-turn shopping with constraint stacking
Handoff from shopping to order management

From Plan to Findings

This case study demonstrates audit planning, not just audit execution. You'll see how to scope an agent, select the right packs, design tasks that trigger multiple agents, and map observations to criteria.

About This Case Study

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Foundation

Agent Experience Assessment

Task Scenario Library

Failure Patterns

Real Audit Example

Downloads & Templates