ADEPTS: A Capability Framework for Human-Centered Agent Design

Pierluca D'Oro; Caley Drooff; Joy Chen; Joseph Tighe

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arXiv · 2507.15885

ADEPTS: A Capability Framework for Human-Centered Agent Design

Pierluca D'Oro·Caley Drooff·Joy Chen·Joseph Tighe

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Abstract

Large language models have paved the way to powerful and flexible AI agents, assisting humans by increasingly integrating into their daily life. This flexibility, potential, and growing adoption demands a holistic and cross-disciplinary approach to developing, monitoring and discussing the capabilities required for agent-driven user experiences. However, current guidance on human-centered AI agent development is scattered: UX heuristics focus on interface behaviors, engineering taxonomies describe internal pipelines, and ethics checklists address high-level governance. There is no concise, user-facing vocabulary that tells teams what an agent should fundamentally be able to do. We introduce ADEPTS, a capability framework defining a set of core user-facing capabilities to provide unified guidance around the development of AI agents. ADEPTS is based on six principles for human-centered agent design, that express the minimal, user-facing capabilities an AI agent should demonstrate to be understandable, controllable and trustworthy in everyday use. ADEPTS complements existing frameworks and taxonomies; differently from them, it sits at the interface between technical and experience development. By presenting ADEPTS, we aim to condense complex AI-UX requirements into a compact framework that is actionable guidance for AI researchers, designers, engineers, and policy reviewers alike. We believe ADEPTS has the potential of accelerating the improvement of user-relevant agent capabilities, of easing the design of experiences that take advantage of those capabilities, and of providing a shared language to track and discuss progress around the development of AI agents.

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Pierluca D'Oro, Caley Drooff, Joy Chen, Joseph Tighe. 2025-07-18. ADEPTS: A Capability Framework for Human-Centered Agent Design. https://arxiv.org/abs/2507.15885

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