Today’s general-purpose models are broadly useful, but they do not have the grounding to effectively execute a specific task like this. (Imagine using ChatGPT today to plan Ezra’s son’s birthday. Maybe it would provide a bit of inspiration, a few theme ideas, but it wouldn’t carry real weight in planning the party.)

“The example I always use in my head is, when can I tell an AI, my son is turning five. He loves dragons. We live in Brooklyn. Give me some options for planning his birthday party. And then, when I choose between them, can you just do it all for me? Order the cake, reserve the room, send out the invitations, whatever it might be.”

In an Agentic UX, an AI agent pulls weight across 3 categories: cognitive, creative, and logistical.

• Cognitive weight — the built-in agent pulls analysis and decision-making weight


• Creative weight — the built-in agent takes on visualization and media creation weight


• Logistical weight — the built-in agent takes tasks on operational and workflow

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For instance, Loom’s AI launch was a great example of going beyond an individual feature to consider the user’s entire journey. It would have been very reasonable for Loom, a video recording solution, to ship an AI transcription feature and call it a day.

We were given the ‘most profound technology since fire,’ and companies have used it to make fake candles.

The last time we switched UX paradigms — from physical to digital — it was much easier to communicate the opportunity to business leaders because “the medium was the messa

We’re entering the agentic era of AI

AI agents have become the next frontier in the evolution of large language models (LLM). In the last few weeks alone, it’s been all the AI leaders can talk about. OpenAI’s Sam Altman called agents AI’s “killer function.” Google’s Demis Hassabis called agents “the next big step change.” Anthropic’s Dario Amodei... See more