The most obvious example is the adoption of the singular 'they' to replace clunky constructions like 'he or she' and 'he/she.' Language purists argue that this is ungrammatical, even though 'they' has been employed in just this way by authors as diverse as Chaucer, Shakespeare, Austen, Dickinson, and Shaw.
In 2021, women held only 28% of professorships in higher education and research institutions, even though they comprised 48% of PhD students, according to data gathered from a sample of 900 EU and non-EU institutions.
For decades in SAAS, products reduced ambiguity. Users supplied constrained inputs, and the system handled the output. It's never been Minority Report cinematic, but it was predictable. By providing predictable environments for manipulating data, users learned by moving things, adjusting variables - and the outcome emerged through interaction.
Algorithms can now transcribe meetings in real time, translate across languages instantly, summarise dense reports in seconds, and generate content tailored to different reading levels. For many users, these are not just productivity gains. They are meaningful improvements in access, sometimes the difference between participating fully and struggling quietly on the margins. Voice interfaces reduce reliance on complex forms. Automated captions support participation in live conversations. Generative tools can rephrase technical or academic language into something clearer and more digestible.
My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow.
My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow. There were no defined user archetypes guiding the query creation process. Team members were essentially reverse-engineering the work: you think of a task, write a query to help the agent execute it, and cross your fingers that it aligns with the needs of a hypothetical "ideal" user - one who might not even exist.
AI is disrupting more than the software industry, and is doing so at a breakneck speed. Not long ago, designers were deep in Figma variables and pixel-perfect mockups. Now, tools like v0, Lovable, and Cursor are enabling instant, vibe-based prototyping that makes old methods feel almost quaint. What's coming into sharper focus isn't fidelity, it's foresight. Part of the work of Product Design today is conceptual: sensing trends, building future-proof systems, and thinking years ahead.
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. In the first part of this series, we established the fundamental shift from generative to agentic artificial intelligence. We explored why this leap from suggesting to acting demands a new psychological and methodological toolkit for UX researchers, product managers, and leaders.
