Defining the UX of
Agentic AI Capabilities

At AWS Bedrock, my work sits where multimodal enterprise data meets increasingly autonomous AI behavior. Rather than building standalone agents, I focus on designing agentic capabilities within RAG systems, such as multi-step retrieval, reasoning, and self-correction across documents, structured data, and other enterprise sources. My role is to translate these capabilities into clear experiences that help people understand what the model is doing, step in when needed, and confidently use AI with sensitive information.

Amazon Bedrock Knowledge Bases

20 months

Timeline

AWS AI (Bedrock)

Platform

Lead designer

Role

My scope and responsibilities

I work at the intersection of UX, Product, Engineering, and Applied Science. My responsibilities include:

  • Leading UX strategy and design for Bedrock Knowledge Bases

  • Translating early AI capabilities into production-ready user experiences

  • Partnering with science teams to shape how model behavior is exposed to users

  • Influencing product direction, not just interface design

Designing for trust and traceability

As retrieval and reasoning becomes more multi-step and agentic, trust becomes a core UX challenge. I focus on:

  • Making model reasoning visible instead of a black box

  • Showing what data is retrieved and why it is used

  • Designing clear points where users can review, intervene, or correct outcomes

Multimodal RAG Evolution

I led the design strategy to expand Bedrock Knowledge Bases beyond text-only retrieval. This work enabled customers to:

  • Use images, diagrams, and diverse data formats alongside documents

  • Reason across multiple data types within a single AI response

  • Unlock insights from enterprise data that was previously hard to query

How I Work

In a fast-moving environment, I establish ways for design to work in parallel with model and feature development. This includes:

  • Prototyping AI workflows and model behaviors alongside data scientists experiments

  • Using design artifacts to explore system behavior, not just screens

  • Helping shape the functional roadmap early, before decisions are locked in

Impact

  • Elevate Bedrock Knowledge Bases from basic retrieval to agentic, multimodal RAG

  • Improve enterprise trust through transparency and traceability

  • Position UX as a strategic partner in AI product development

Strategic Reflections

Designing at the Intersection of Science and UX

Working closely with data scientists was new territory for me at first, but it quickly became one of the most important partnerships in this work. What I learned:

  • In generative AI, user experience is often shaped by how the model reasons internally

  • Design decisions cannot be separated from model behavior, retrieval logic, or system constraints

  • Trust and usability depend on making invisible AI processes visible to users

My role was to bridge design and science by:

  • Translating model capabilities into clear user mental models

  • Helping surface complex retrieval and reasoning in ways that felt intuitive and reliable

  • Ensuring technical depth did not come at the cost of usability

Introducing New AI Paradigms into a Mature Platform

Building agentic RAG capabilities inside an established platform like AWS Bedrock came with unique challenges. The tension I navigated:

  • Pushing forward new, non-linear AI workflows

  • While maintaining the stability, predictability, and compliance enterprise customers expect

My focus was on:

  • Integrating agentic reasoning into a structured, API-driven ecosystem

  • Designing experiences that support multi-step retrieval and reasoning without overwhelming users

  • Preserving consistent performance and clear boundaries as system complexity increased

Designing Mental Models for Frontier Technology

As AI systems become more capable, clarity becomes more important, not less. I focused on:

  • Defining mental models that help users understand what the system is doing and why

  • Making advanced AI behavior feel like a natural extension of an existing platform

  • Ensuring new capabilities felt robust, dependable, and enterprise-ready

This is the space where I do my best work: translating frontier technology into experiences that feel thoughtful, trustworthy, and seamlessly integrated into world-class ecosystems.

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