Bug Tracking in the AI Era
Why we built FeedbackFalcon and how AI is changing the way we fix software.
You know the routine. You’re working in your editor, making good progress, and then a Slack ping comes in.
“Hey, the checkout button looks weird and nothing happens when I click it.”
Attached is a cropped, blurry screenshot.
The context gap
You open Jira to find out what browser they’re using. You dig through the codebase trying to map an auto-generated CSS path like #root > div.page-transition-wrapper:nth-child(2) > main > div.hero to actual React components.
You often spend 45 minutes trying to reproduce a bug that only takes 2 minutes to fix.
This has basically been the accepted cost of doing business. We relied on bug trackers that charged per-seat but still left engineers guessing about what actually broke.
AI needs context
We’re shifting to AI-assisted software development. IDEs like Cursor and Windsurf help write code quickly, but AI agents share a core weakness with human engineers: they can’t see the client’s local environment. An LLM can’t fix what it can’t understand, and raw screenshots or coordinate-based CSS selectors don’t help.
We built FeedbackFalcon to provide this context.
Capturing “visual feedback” isn’t enough anymore. To debug effectively, you need the technical details.
When a client reports an issue, FeedbackFalcon doesn’t just take a picture. It captures the semantic Accessibility (A11y) tree, the silent console.error crashes, and the 500 Internal Server Error in the network tab.
Think outside the ticket
Instead of switching between a browser, a project management board, and your terminal, the bug details can come directly to your editor.
Using our native Model Context Protocol (MCP) server, your AI agent pulls the exact context of the client’s session into your IDE. It reads the failed API payload, understands the semantic structure of the page, and generates the fix.
No more brittle selectors, and no more guessing about local state.