analiza aiqbench
Affora introduces a design system enabling computer-use agents to interpret human interfaces without requiring separate agent-specific surfaces, thereby bridging user experience and machine readability. Controlled studies demonstrate that preserving semantic meaning allows for substantial visual variation while maintaining performance, offering reusable implementations to address current interface deficits. This approach represents a significant step toward scalable AI integration within existing software ecosystems rather than creating parallel digital environments.
Affora: A Design System for Agent-Friendly Interfaces
arXiv:2609.19125v1 Announce Type: cross Abstract: Computer-use agents increasingly operate software designed for people, but interfaces often leave actions or task state unclear to machine readers. We present Affora, a design system that supports both readers while preserving visual freedom and familiar human workflows. Three controlled studies examine component implementations, visual variation, and interaction-design principles. Their findings inform guidance from individual components to complete sites, supported by reusable implementations and executable checks. Agent performance depends on the interaction meaning available through its interface representation; substantial visual variation remains possible when that meaning is preserved. Evaluation on independently authored interfaces shows gains where Affora addresses existing deficits, but limited effects where those deficits are absent or outside its coverage. A workflow case provides preliminary evidence of reduced interaction cost. Affora connects user experience and agent experience through a shared interface rather than a separate agent-only surface.
Abstract:Computer-use agents increasingly operate software designed for people, but interfaces often leave actions or task state unclear to machine readers. We present Affora, a design system that supports both readers while preserving visual freedom and familiar human workflows. Three controlled studies examine component implementations, visual variation, and interaction-design principles. Their findings inform guidance from individual components to complete sites, supported by reusable implementations and executable checks. Agent performance depends on the interaction meaning available through its interface representation; substantial visual variation remains possible when that meaning is preserved. Evaluation on independently authored interfaces shows gains where Affora addresses existing deficits, but limited effects where those deficits are absent or outside its coverage. A workflow case provides preliminary evidence of reduced interaction cost. Affora connects user experience and agent experience through a shared interface rather than a separate agent-only surface.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
arXiv:2609.19125 [cs.HC]
(or arXiv:2609.19125v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2609.19125 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 239 cuvinte
Subiecte conexe

Claude Code’s Next Era — Thariq Shihipar, Anthropic
Latent Space · 29 sept. 2026

The SaaSpocalypse that wasn’t, with Atlassian CEO Mike Cannon-Brookes
The Verge · 28 sept. 2026

[AINews] Opus 5.5 is good at explainer videos
Latent Space · 29 sept. 2026

Brian Chesky interview: AI agents need their own operating system
TechCrunch · 1 oct. 2026