Google’s Lighthouse 13.3 now includes an experimental ‘Agentic Browsing’ audit that scores how ready a site is for AI agents like ChatGPT or Gemini. The audit checks llms.txt, WebMCP registration, accessibility tree health, and layout stability, offering wins but warning that some signals (e.g., llms.txt) provide no SEO benefit. Implementing these standards helps AI assistants interact with your site reliably.
A new Figr blog explains how AI‑powered workflow tools keep product requirements, design briefs, and implementation details linked, preventing the usual miscommunication between product managers and designers. By automating context‑preserving handoffs, from PRDs to mockups and back, teams cut cycles of clarification and maintain design quality despite AI‑generated screens.
Token‑based AI pricing creates scarcity cues that make users behave like arcade‑goers, hoarding prompts and feeling anxiety over limited credits. The article outlines how UX patterns, transparent usage meters, graceful rationing, and nudges, can either amplify or ease these psychological effects in AI products.
Most AI tools lose users after the first try not due to weak models but flawed adoption design. The article outlines four failure modes, activation cliffs, trust gaps, broken correction loops, and missing progressive disclosure, and offers design patterns to keep users engaged.