For the last year, the whole effort in AI coding tooling has gone one direction: helping the agent see your screen. Paste a screenshot. Crop the bug. Wire up an MCP screenshot tool so it can capture any window itself. All of it pushes pixels from you to the agent.
That is only half a loop. And it is the easier half.
The half nobody built
Here is the question screenshots never answer: what did the agent actually understand?
You show it the screen, it goes quiet, and the next thing you see is an output — code, a layout, an edit. If its interpretation matched yours, great. If it didn't, you only find out after it has built the wrong thing, and now you are back in the expensive correction loop: "no, not that element, the other one." The agent's understanding stayed invisible right up until it became the wrong result.
We have made agents good at receiving visual context. We have done almost nothing to make their intent visible before they act.
Annotation should go both ways
The fix is to let the agent annotate back. Before it writes a line of code, it draws its plan onto your live screen — "I'll make this the nav, this hero goes full-width, this is the primary CTA, this row becomes a 3-up grid" — as labeled marks you can actually see. Then you do what you are good at: you glance, you correct the two things it got wrong, and you send that back. Then it builds.
It is the difference between a contractor who pours the foundation and asks how you like it, and one who sketches on the blueprint first. The first is the loop we have. The second is the loop we want.
This makes the agent's interpretation a visible, editable artifact — something you can veto in seconds instead of discovering in a diff. It is a guardrail as much as a feedback channel: the agent has to commit to an interpretation you can see before it spends effort on it.
Why this is the cheaper loop, too
Catching a misunderstanding at the intent stage is dramatically cheaper than catching it at the implementation stage. A plan sketch is a handful of labeled boxes; a wrong implementation is a full generation you now have to throw away and redo. As covered in why AI coding agents burn tokens on visual bugs, every correction round re-sends your whole context and re-bills it — so the round-trips you avoid by validating the plan up front are the expensive ones. We put real numbers on that in annotate, don't regenerate.
It also fits what we already know about annotation: a labeled region is structured context a model can reason over, not a blob of pixels it has to interpret. Bidirectional feedback just applies that idea to the agent's own output instead of only yours.
What this looks like in practice
This is the bidirectional visual feedback loop Screentack is built around. Over MCP, your agent can draw its understanding directly onto your screen with labeled shapes; you flip into review, comment on or move what's wrong, and send it back — and the agent reads your corrections before it acts. The same engine that lets you hand the agent visual context now lets the agent hand its understanding back to you.
Screenshots taught the agent to see. The next step is letting it show you what it sees — and letting you fix it before it builds.
Want a coding agent that shows you its plan before it writes the code? Download Screentack — a free 7-day trial, then $29 once.