NN/g renamed its field to experience design because agents now act for people

The screen is losing its spot at the center of your product. People are starting to tell an agent what they want, and the agent goes off and does it across tools. That changes what your team actually makes. Let me catch you up on what moved and what to bring to your next review.

The deep cut

• People center the work, not screens. NN/g renamed the field "experience design" because agents now act for humans, and the goal still ends with a person.
• Context is not the whole UX job. Anthropic's playbook reduces UX to Markdown files and skips user research entirely.
• The person using the tool owns the call. A researcher caught an invented quote in a minute that no contract manager would.

What your team makes when there are fewer screens

The shift is concrete. Eddie Lobanovskiy at Unfold says a task that once took five screens and ten clicks now needs three moments: a prompt, a confirmation, and a result. His team still ships screens, just fewer of them. The new deliverables never appear on a screen at all. They define how an agent behaves when a request is vague, which actions it can take alone, and which need a person's sign-off.

The hard parts move into handoffs. Raluca Budiu at NN/g walks through a flight booking where the agent works fine and the human request is reasonable, but the task breaks when it passes back to the person whose password manager is missing in the agent's browser. You can't spot that by studying the human or the agent alone. The problem lives in the seam between them.

The playbook that writes your team out

Here is the catch for anyone adopting an AI-native workflow. Tanner Kohler at NN/g read Anthropic's August 2026 playbook and found UX owns no stage and no approval gate. UX gets reduced to "context": brand rules and design standards stuffed into Markdown files that guide the AI. Context is real, but it only encodes what you already know.

What drops out is discovery and evaluation. The playbook names the product owner to mock up interfaces with an AI tool, then pass them to a coding agent. Kohler's point is blunt: an untested mockup is UI work, not UX, and the playbook makes no mention of user research at all. His worst case is a feature that ships, gets abandoned, and shows no distress in monitoring because the code runs fine. The problem was bad labeling. Nothing was watching for that.

The tool automating its own owner

There's a trap underneath the excitement. A thread on r/UXDesign describes designers who built a strong design system for agents and automated their own role into irrelevance. The work was good. It just didn't communicate its value to a company that reacts to quarterly numbers.

The guard against this is knowing your own standard well enough to teach it. A designer writing for Muzli borrows a line from Lauren Tan at Cursor: stop being the home cook who does all the chopping and become the chef who runs the kitchen. But teaching a chef your standard means you can describe that standard first. Kursat Ozenc found the same limit building a music tool he had no expertise to judge. AI lowered the floor for making tools. It did not lower the bar for knowing whether they're any good.

Who gets to pick the tools

A real fight is starting over who sets AI strategy. Patrick Neeman argues the people using the tools should own the call, not the operations team buying contracts. His proof is the researcher who dropped 12 transcripts into a synthesis tool and caught a fabricated quote in under a minute, because she ran six of those interviews. The contract manager would never have caught it.

The numbers back the pattern. In Microsoft's 2024 Work Trend Index, 78% of people using AI at work brought their own tools. Every shadow tool is a vote from someone who can tell good output from bad. Neeman's fix is a split: subject matter experts own which tools get adopted and what counts as acceptable output, operations owns security, contracts, and budget. Seats are giving way to tokens, and deciding which work deserves the priciest model takes someone who knows the work.

Three questions for your team

• If your company adopted an AI-native workflow tomorrow, where does discovery and evaluation live? If the answer is "the context file," you've already lost the user insight that file depends on.
• Can each of your designers write down their own standard of good, clearly enough to teach an agent? If not, that's the skill to build before the tooling, not after.
• Who on your team can catch a plausible-but-wrong output in under a minute? Give those people the decision rights on AI tools, and hand operations the contracts and security.