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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.
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Affora: A Design System for Agent-Friendly Interfaces

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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)

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