Your AI coding agent can be brilliant and expensive at the same time. The expense is rarely the clever part — it is the repetition. And the single most repetitive, token-hungry failure mode is the one nobody measures: visual bugs the agent cannot see.
Where the tokens actually go
Agentic coding tools like Claude Code and Cursor work in a loop. On every turn, the model re-reads a large chunk of context — your files, the conversation so far, tool output — to decide the next step. That context is re-sent and re-billed each turn. So the real driver of cost is not any single message; it is the number of turns a task takes.
Most tasks converge quickly. Visual ones do not.
Why visual bugs are the worst offenders
When the bug is "this button is misaligned" or "the generated image looks off," the agent is flying blind. It changes some code, you look at the screen, and then comes the expensive part:
"No, the other one. The blue one, top-right. Below the header. A bit lower."
Each of those corrections is another turn — another full re-send of your context, another round of generation. A UI nudge that should take one turn can stretch to five or six, and you pay the context cost every single time. Multiply that across a session and the "cheap" visual tweaks quietly become your biggest line item.
The fix: get it right on the first turn
The way to cut the cost is not a smaller model or a cheaper plan — it is fewer round-trips. And the way to get fewer round-trips on visual work is to hand the agent precise context the first time, instead of letting it guess:
- A tight crop of the exact region that is wrong, so the model is not hunting through four megapixels of UI.
- The full frame too, so it still understands layout and surroundings.
- A spatial manifest — text that says which region is which and where it sits, e.g. region-2, "misaligned button," at x 71% y 64% of checkout.app.
With that, the agent reasons over facts instead of guessing, and lands the fix in one turn. You stop paying for the "no, the blue one" conversation entirely.
An honest note on "saving tokens"
A precise capture is actually richer than a bare screenshot — it includes the crop and the manifest, so a single message can be a bit larger. The savings do not come from sending less in one turn; they come from needing far fewer turns. Since every turn re-sends your whole context, cutting a five-turn fix down to one is where the real money is saved — and a focused crop is still cheaper in image tokens than dumping a full-screen capture every time.
How Screentack does it
Screentack is built for exactly this. You drag regions over what is wrong, label them, and it sends the full screenshot, every crop, and the spatial manifest to your agent in one paste — or lets the agent capture your screen itself over MCP. The result is the one you actually want: better fixes on UI and visual work, in fewer iterations, for fewer tokens. It is macOS-native, private and on-device, and a one-time purchase.
For the mechanics of how structured visual context works, see how to give your AI coding agent visual context.
Tired of paying for guesswork? Join the early-access waitlist.