Generative UI

Thesis

Scalable, portable, and robust generative architectures demand strict domain isolation between reasoning and rendering.

Decoupling backend inferencing from frontend rendering establishes true domain alignment.

By delegating interface orchestration to a localized frontend UI Agent, backend models output only pure semantic intent, resulting in highly reliable, universally portable intelligence.

Separation of Concerns

Information Agents should focus on:

  • Reasoning

  • Planning

  • Producing structured output

Generative UI should focus on:

  • Interpreting agentic output

  • Selecting the appropriate components

  • Managing human interaction, approval, and feedback

Approach

We apply a Generative UI architectural pattern that transitions the AI application layer from probabilistic inference (an "Inferred UI") to an intelligent execution model. It imposes a strict separation of concerns and applies the familiar Model-View-Controller (MVC) design pattern to Generative UI:

  • The Model (The Information Agent): The backend agentic process executing tasks and outputting semantic data.

  • The View (The Component Registry): The agnostic UI components that serve as the human-agent touchpoints.

  • The Controller (The UI Agent): The Semantic Presentation Router that translates the agentic payload into the appropriate View.

Research & Development

Generative UI agent interfaces capable of presenting UI-agnostic agentic payloads.

Asynchronous task and workflow AI agents operating with objective-level specificity delivering UI-agnostic payloads consumable by generative UI agent interfaces.