Developing effective agentic AI requires a new research playbook. When systems plan, decide, and act on our behalf, UX moves beyond usability testing into the realm of trust, consent, and accountability. Victor Yocco outlines the research methods needed to design agentic AI systems responsibly.

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Agentic AI represents a shift from reactive tools to autonomous systems that plan, execute, and persist in tasks on behalf of users. Unlike traditional automation or generative AI, these agents make decisions and take proactive actions, requiring new UX research methodologies focused on trust, consent, and accountability. The article introduces a four-level taxonomy of agent autonomy (observe-and-suggest, plan-and-propose, act-with-confirmation, act-autonomously) and outlines specialized research methods including mental-model interviews, agent journey mapping, and simulated misbehavior testing. Key metrics like intervention rate, rollback rates, and time to resolution help measure agent performance. Designers must address risks like "agentic sludge" and false competence through transparency, exposing the logic primitives behind agent decisions to maintain user control and trust.

Beyond Generative: The Rise Of Agentic AI And User-Centric Design — Smashing Magazine

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