Before an AI Agent Acts, Ask for a Change Preview
An AI agent’s plan is not an exact preview of its actions. Test a workflow that binds approval to specific changes, detects stale versions and makes recovery limits visible.
Human-centred technology—from everyday life to deep engineering.
An AI agent’s plan is not an exact preview of its actions. Test a workflow that binds approval to specific changes, detects stale versions and makes recovery limits visible.
AI assistants often complete an underspecified task instead of exposing the missing choice. A one-question budget can reduce rework without turning every request into an interview.
Weak Wi-Fi can reveal the hidden radio landscape of a home. A simple three-point test separates broadband trouble from walls, interference and poor router placement.
AI personas can widen a design team’s hypotheses, but fluent simulation is not lived evidence. Compare synthetic predictions with real observations before using them to decide.
Persistent AI memory saves repetition but can preserve stale assumptions. Build a Memory Boundary Card to govern purpose, source, correction and expiry.
Support demand is not merely overhead. Repeated calls, abandoned forms and unnecessary visits can reveal defects that transaction dashboards never see.
Human–AI agreement can hide anchoring or stubbornness. A better workflow preserves both provisional views, examines divergence and records why judgement changed.
A backup can preserve every byte and still fail the person who needs it. A small recovery rehearsal reveals missing permissions, unreadable formats and knowledge trapped in one person's head.
A documented in-car comparison shows why a well-designed physical control can preserve tactile skill, reduce visual demand and keep human attention on the road.
A device can remain physically sound yet become useless when its app, API or cloud service changes. Follow the dependency stack—and learn what predicts a longer life.