AFT Design Knowledge Hub

Context

Following a significant reduction in force at Amazon, our design team was left with substantially less capacity while expectations and scope remained unchanged. At the same time, critical product and experience knowledge—including UX research, design system documentation, accessibility guidance, and fulfillment center operational context—was fragmented across Quip, wikis, dashboards, and internal tools.

The disbanding of the Common Experience (CE) program further increased the risk of institutional knowledge loss. Discovering prior work was slow, duplicated effort was common, and builders were spending disproportionate time searching for context instead of solving problems.

Problem

AFT’s organizational knowledge lived in silos, making it difficult for teams to answer even basic questions consistently:

• Where does relevant UX research already exist?
• What design system guidance applies to this use case?
• How do operational constraints affect this workflow?
• What standards or requirements must this design meet?

Without a shared, accessible source of truth, builders across roles duplicated research, misinterpreted standards, and relied heavily on tribal knowledge—an unsustainable model given reduced headcount.

Approach

I originated and authored the AFT Design Knowledge Hub as a unified system that brings together AFT’s most critical design knowledge and makes it accessible through AI-powered agents.

While the system strongly supports design workflows, it was intentionally built for any AFT builder. Engineers, product managers, program managers, ACES partners, and operations leaders can all query the same source of truth and receive answers grounded in shared standards, research, and operational reality.

The system is built on a Retrieval Augmented Generation (RAG) architecture and integrates UX research, design system documentation, accessibility requirements, and fulfillment center operational guidance into a single, queryable knowledge base.

Knowledge base foundation

Content is structured into intentional domains—UX tenets and traps, Alchemy design system documentation, UX research, accessibility standards, content guidelines, and fulfillment standard work—preserving semantic relationships while remaining current and discoverable.

Agentic design support

On top of the knowledge base, I designed a set of specialized AI agents that act as supporting cast members for AFT builders across disciplines:

• AFT Design Assistant — synthesizes research, operational context, and system guidance to support complex design problem-solving
• AFT Design Reviewer — provides structured UX, accessibility, and content feedback on designs and prototypes
• AFT Accessibility Guardian — focuses on accessibility compliance and inclusive design guidance
• AFT Content Crafter — helps create and refine UI copy aligned to AFT and Alchemy standards

These agents are intentionally framed as collaborators—not replacements for human judgment—designed to handle repeatable, pattern-based work while freeing designers to focus on higher-order problem solving.

Implementation

I built and shipped the entire system independently using Amazon Bedrock, OpenSearch Serverless, and internal tooling. Bedrock acted as an implementation guide as I worked through architecture decisions, data ingestion strategies, and agent behavior design.

This was my first end-to-end application of agentic design and delivery. The work involved deep experimentation, learning-by-doing, and iterating directly in production—resulting in a live system supporting AFT teams today.

Outcomes

• Centralized access to AFT design, research, accessibility, and operational knowledge for all builders
• Self-service UX guidance available to Designers, PMs, Engineers and partners
• Reduced reliance on meetings and synchronous design support
• Faster discovery of existing research and standards
• Strong leadership interest in agent-enabled design enablement

Reflection

This project changed how I think about my role as a Designer. I feel empowered to look beyond screens and immediate customer problems and focus on how my teammates solve problems on behalf of our customers.

AI has fundamentally changed how I work—and how I expect to work going forward. Designing systems that scale knowledge, judgment, and craft has become a core part of my practice, and I’m energized by where this path leads next.