How to Make Your AI Agent See What You See

To make your AI agent see what you see, you must stop describing UI elements with words and start exporting them as structured JSON and DOM selectors. Textual descriptions fail because models don't share your spatial awareness; they need the exact file path, CSS selector, and viewport coordinates to actually fix the code.

Stop typing descriptions and start dropping markers

The fastest way to fail with an AI coding assistant is by typing, "the blue button near the top right, no, the one below the header." It’s an exercise in futility. You’re wasting your time, and the model is hallucinating the wrong component. Instead, you need to use a tool that maps your screen to the underlying source code. When I’m debugging a React component, I don’t explain the layout. I use markagent to click the element, capture the src/components/Navbar.tsx reference, and generate a prompt that tells the AI exactly which file to open and which selector to target. You’re not just showing the AI an image; you’re giving it the coordinates to the crime scene.

Visual context ai is only as good as the selector

You can’t rely on generic screenshots for complex frontend work because a static image lacks the CSS hierarchy needed for a reliable patch. If you’re just pasting a PNG into Claude or Cursor, you’re missing the DOM context. You need a tool that extracts the stable CSS selector—like div.nav-wrapper > button.active—alongside the visual data. This is what separates a "fix" from a "guess." When I use markagent, I get the exact component tree. If I need to change a padding value on a specific modal, I don't guess the class name. I click, the extension grabs the data-testid or the BEM class, and the agent knows exactly what to mutate.

Leverage DOM context to stop hallucinating

AI models hallucinate when the visual context ai is disconnected from the codebase. If you’re trying to show an AI agent your screen without providing the actual file path, you’re setting yourself up for a ReferenceError. The model sees a button, but it doesn't know if that button lives in Button.tsx or App.js. By using an extension that reads the React fiber tree, you bridge the gap between pixels and code. Once you have the file path and the line number, the agent stops guessing. It goes straight to the file, applies the diff, and saves you three back-and-forth prompts.

How to show ai my screen without the privacy risk

You don't need to upload your entire UI to a cloud-based screenshot tool to show ai my screen. Privacy matters, and most "AI-ready" screenshot tools are just glorified cloud storage buckets that leak your IP. Keep your workflow local. When I capture a UI state, I use an extension that stays in the browser. The data—the DOM nodes, the file paths, the screenshots—stays on your machine. You generate a markdown block, copy it, and paste it into Cursor or Claude Code. No server logs, no external databases, just local data moving from your browser to your IDE.

Use numbered play-throughs for complex flows

Complex bugs rarely live in a single component; they live in the user journey. If you’re trying to debug a checkout flow, don't send one screenshot. Use a numbered sequence to map the interaction. Click the cart, click the checkout button, click the payment field. By creating a numbered playthrough, you give the agent a map of the state machine. Each step should be paired with its own DOM context so the agent can trace the flow from the initial click to the final validation error. It’s the difference between saying "it's broken" and saying "the function at line 42 fails when this specific state is active."

Tune your export for your specific agent

Not all AI coding agents speak the same language, so you have to tune your export format. Claude Code wants different metadata than OpenCode or Antigravity. If you’re just dumping a generic block of text, you’re forcing the AI to parse garbage. Use an extension that lets you toggle the output format based on your target tool. I keep my markagent settings pinned to "Cursor" mode because it formats the file path and the CSS selector in a way that the IDE’s internal agent understands immediately. Don't make the AI work to understand your prompt; format the prompt so the AI can execute it immediately.

Stop the back-and-forth debugging loop

The ultimate goal is to kill the "AI didn't understand the context" loop. Every time you have to re-explain a layout issue, you've failed the prompt engineering process. By providing visual context and structural DOM data in one go, you make the fix a one-shot operation. You click the element, the extension generates the prompt, you paste it, and the agent writes the code. If you aren't doing this, you're doing it the hard way.

Stop describing the UI. Start mapping it.