In a world where AI is informing more design choices, it’s easy to mistake predictions for certainties. This article introduces Probabilistic Design, a mindset that allows UX and product teams to accept uncertainty, decipher AI outputs with nuance, and make smart, adaptive decisions.
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We have officially moved past the era of humanoid robots as mere public relations stunts. As they become increasingly lifelike, society may soon face profound social, psychological, and ethical challenges. What happens when the boundary between humans and machines becomes almost impossible to distinguish?
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Practical guide on how to reduce drifts, minimize mistakes, maintain context, and improve the quality of AI-generated prototypes. Brought to you by Design Patterns For AI Interfaces, friendly video course on UX and design patterns by Vitaly.
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Why traditional loading patterns like spinners fail in agentic AI experiences, and how interface patterns that reveal the system’s process, status, and decision-making can improve transparency and build user trust.
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AI tools are eliminating the need to “bug” colleagues for help, but the informal interactions they replace are the very scaffolding that builds team trust, belonging, and innovation. Casey Hudetz and Eric Olive explore the research and potential impacts behind that risk and offer practical strategies for maintaining human connection while leveraging AI’s strengths.
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Designing for agentic AI requires attention to both the system’s behavior and the transparency of its actions. Between the black box and the data dump lies a more thoughtful approach. Victor Yocco explores how to map decision points and reveal the right moments to build trust through clarity, not noise.
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UX design is entering a new phase, with designers shifting from makers of outputs to directors of intent. AI can now generate wireframes, prototypes, and even design systems in minutes, but UX has never been only about creating interfaces. It’s about navigating ambiguity, advocating for humans in systems optimised for efficiency, and solving their problems through thoughtful design.
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Autonomy is an output of a technical system. Trustworthiness is an output of a design process. Here are concrete design patterns, operational frameworks, and organizational practices for building agentic systems that are not only powerful but also transparent, controllable, and trustworthy.
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AI coding tools like agents can be valuable allies in everyday development work. They help handle time-consuming grunt work, guide you through large legacy codebases, and offer low-risk ways to implement features in previously unfamiliar programming languages. Here are practical, easy-to-apply techniques to help you use these tools to improve your workflow.
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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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