An archive of decisions, failures, changes, and shipped work. The record stays useful by keeping the sequence—and the limits—visible.
What a button inside an AI-generated answer can actually do, and how to distinguish a visual control from an authorized change to another system.
Imagine asking an AI assistant what to do with excess inventory. It returns a neat table: move twelve units from Store A to Store B. Below it is a button labelled Approve transfer .
Synthetic UX participants can sharpen a research plan. Their plausible answers do not, by themselves, establish what real users did, felt or needed.
An analysis of why AI-assisted editing needs evidence for both the requested mutation and the constraints the edit was not authorized to change.
An analysis of why AI-product latency and output variance need distribution-level measurement instead of being hidden behind healthy averages.
Embedding an agent in Slack, mobile, or another work surface changes more than the interface: it changes context, identity, triggers, visibility, authority, and the record left behind.
An analysis of reviewable AI edits, exact proposal state, approval granularity, and the boundary between generating a change and making it authoritative.
A product-experience analysis of what changes when a website exposes structured tools directly to agents: less interaction guesswork, but a new contract for semantics, authority, validation, and inspectable results.