Move work forward.Keep what you learn.
Exploring AI products that carry context forward, move the task along and bring the result back. Every task should give the next one a place to start.
Start with a questionThe numbers add up. Why is the answer wrong?
The same month. The same orders. Change which delivery date counts, and the answer changes. Check it yourself: what did the agent miss?
100 orders · 15 orders change status when the delivery-date definition changes.
Orders on time 94 / 100
Fictional case · Interactive study · Point at any cell to see that order’s three dates.
The next task shouldn’t start from zero.
Gather the material. Explain the background. Check the answer, chase progress, redo the work. Then repeat it all for the next task. AI products should account for this effort too—and leave behind what the next task needs.
Remember the result too.
Keep what was done and what came of it in one record. Only when results come back does the next piece of work get better.
A memory that knows the result becomes the basis for verification.
Being able to say it is right.
A correct calculation is not a correct answer. Show what it was checked against, and what is still unknown.
Explore verificationWith a grounded answer, who acts next, and how far?
Decide what it may do.
See, propose, act. Define what an agent may do as permissions, and return to a person, with evidence, at the boundary.
The decision and its reason become the next thing to remember.
Reliable results. Less effort from you.
Count the explaining, checking, chasing and rework alongside the quality of the result.
Every handoff should reduce the burden of getting the work done.
And someone answers for the result: people set the goal and the scope of authority. The system acts within it, checks again when evidence is missing, and returns decisions that need human judgement.
These interactive studies use fictional scenarios to make design choices inspectable. They demonstrate mechanisms, rather than measured savings or a live AI service.