Discover how Webvizio's innovative approach gives AI coding agents the context they need to accurately fix bugs, bridging the gap between users and technology.

How to Stop AI Coding Agents from Hallucinating and Start Fixing Real Bugs

Let’s be real: spinning up a new component or a boilerplate MVP with an AI coding assistant like Cursor is the easy part. It feels like magic. But the real challenge—the part that makes you want to pull your hair out—is getting it to fix a specific bug or make a small UI tweak without going completely off the rails.

You type in, "The submit button is slightly misaligned on desktop." The AI cheerfully refactors your entire form, introduces three new bugs, and somehow changes the button color to neon green. Sound familiar? 😬

This gets 10x worse when bug reports come from non-technical folks. A ticket that says "the login button is broken" is about as useful as a broken pencil (pointless, right?). It’s the "garbage-in, garbage-out" problem wrapped in a bright, shiny package.

To solve this, we at Webvizio built a tool that bridges the gap between the person reporting the issue and the AI that will (hopefully) fix it. It’s all about context! 🔍

The Core Problem: AI Is Flying Blind

An AI coding agent doesn’t “see” your website. When you ask it to fix a bug, you're essentially describing a visual, interactive problem to someone over the phone who has never seen your app. It misses the critical context that a human developer takes for granted:

Visual Context: What does the UI actually look like? Where is the element on the page? Is it covered by something else? 🤔
Runtime Context: Are there console errors? What did the network tab say? What's the state of the application?
Environmental Context: What browser, OS, and screen size is the user on?
DOM Context: What's the specific element? What are its CSS properties, parents, and siblings? A vague description like "the blue button" just won't cut it.

Without this information, the AI is just winging it based on your code. And often, it guesses wrong—leading to some pretty wild results! 🙈

Our Solution: A "Context Capture" Bridge

We built a tool to act as the eyes and ears for our AI assistant. It’s designed to let our non-technical team members report issues with zero friction while gathering all the technical data a developer (or an AI) would need.

Here’s the workflow:

Point, Click, and Report: Non-technical team members (like our marketing peeps or project managers) can browse a staging or live webpage. When they spot an issue, they use our browser extension to simply point and click on the problematic element. They can leave a plain-English comment like, "This button doesn’t submit the form when I click it."
The Magic Under the Hood: The moment they submit that report, the extension automatically captures a rich payload of technical context. It’s like a developer looking over the user’s shoulder and includes:
- Annotated Screenshots: Highlighting the user’s click.
- Console Output: All errors, warnings, and logs at the moment of the report.
- User Actions: A breadcrumb trail of recent clicks and inputs.
- Precise Element Selectors: The unique CSS selector for the flagged element and all its parents.
- Technical Specs: Browser version, operating system, viewport size, etc.
Structuring and Storing the Context: All this data is organized into a structured task within our system. Instead of a messy email or vague ticket, we get a comprehensive bug report with every piece of technical data neatly categorized. This creates a single source of truth for the issue.
Connecting the AI via the MCP Server: We created our own MCP server that acts as a context provider to the AI coding agent. When developers are ready to address a bug, they don’t even have to copy-paste. They simply inquire all available tasks through the AI agent chat and receive the user’s comment, screenshot, console logs, DOM selectors, and browser info in one clean API response. 🛠️

The Result: From Guesswork to Precision

With this direct, automated connection, the AI now has everything it needs to understand the problem deeply. When it receives a task, it knows exactly which button the user is talking about, it sees the precise TypeError from the console, and it understands the exact context of where the bug occurred.

The AI's proposed solution is now laser-focused and exponentially more likely to be correct on the first try. It’s no longer about guessing; it’s diagnosing based on a rich dataset. 🎯

This has practically eliminated the "Can you send me a screenshot of the console?" back-and-forth for us. The feedback loop is tighter, fixes are faster, and developers can focus on implementing solutions instead of playing detective.

If you'd like to see it in action, we've got a short demonstration video on YouTube:

Watch it here!

Let's Open the Floor: How Do YOU Do It?

This is how we're tackling the context problem, but I know we’re not the only ones facing it. I’m genuinely curious to hear how others handle this, especially with non-technical teams:

If you’re a solo dev, how do you keep context straight when working on AI-generated code?
If you're in a team, what’s your process for turning vague bug reports from a PM or a client into actionable tasks for an AI agent? Templates, forms, or tools? 🔧

Your thoughts are welcome in the comments below!