Agents hate friction: early thoughts on building for agents

In exploring the evolving landscape of designing for language learning models (LLMs) and agents, the text highlights the challenges and considerations of creating user experiences (UX) that cater to non-human users. While traditional software and hardware design has focused on human-centric interfaces, the rapid development of LLMs necessitates a shift toward agent-first design, recognizing agents as the primary users. This involves understanding the nuanced differences between human and agent interactions, especially in text-based interfaces, and addressing issues like friction, which can lead to agents opting out of tasks they find cumbersome. The text underscores the importance of testing and adapting products to ensure agents do not discard them due to inefficiencies, focusing on dimensions such as steps to completion, self-correction, consistency, context efficiency, token efficiency, and the strength of familiarity. This approach is crucial as agents increasingly influence how products are discovered, used, and evaluated, with the text suggesting that while some principles may be effective now, they are subject to change as models evolve.

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