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How to Debug Browser Errors with an AI Coding Agent

When you need to debug browser errors, working with an AI coding agent should be faster than doing it alone. In practice, it rarely is. You open DevTools, scan the console, copy the error message, copy the stack trace, paste it all into your AI assistant, then spend another minute explaining what page you were on and what you clicked. The AI finally has enough context to suggest something -- but by then you have already lost the thread of what you were building.

It gets worse with intermittent errors. A race condition that only triggers on fast repeated clicks. A null reference that appears when the API is slow but not when it is fast. An error that vanishes the moment you open DevTools because the act of inspecting changes timing. These are the bugs that eat hours, and the manual copy-paste workflow makes them nearly impossible to hand off to an AI assistant with enough fidelity.

And then there are cascading failures. One API endpoint returns a 500. That causes three React components to throw TypeErrors because they received undefined instead of an array. The console fills with a wall of red, and when you paste the first error to your AI, it fixes the symptom instead of the cause -- because it never saw the API failure that started it all.

The Traditional Approach​

The manual workflow for AI frontend debugging typically looks like this:

  1. Something breaks in the browser
  2. Open DevTools, find the Console tab
  3. Copy the error message
  4. Copy the stack trace (if you can find the relevant part among 30 lines of webpack internals)
  5. Switch to your AI assistant and paste it
  6. Describe what you were doing: "I was on the dashboard, I clicked the filter dropdown, selected 'Active', and then this error appeared"
  7. Describe the page state: "There were about 20 items in the list, and I had already filtered by date"
  8. AI asks: "Can you show me the network tab? Were there any failed API calls?"
  9. Switch back to the browser, check the Network tab, copy the failed request details
  10. Paste those too
  11. AI suggests a fix... for the TypeError, not the API failure that caused it

By the time you reach step 11, five minutes have passed and the fix addresses a symptom. The real bug -- a missing null check in the API route handler -- is still there.

The agnt Approach​

agnt is a browser debugging tool that gives your AI coding agent direct access to browser errors. It sits between your browser and your dev server as a reverse proxy. Every JavaScript error, HTTP failure, and server-side exception is captured automatically. Your AI queries all errors at once, with full context, without you touching the browser console.

Here is what that looks like in practice.

Before (manual copy-paste):

You: "I'm getting a TypeError on the dashboard page. The error says
'Cannot read property map of undefined' at ProductList.tsx line 42.
Also there might be a network error but I'm not sure if it's related."

After (with agnt):

AI: get_errors {}

=== Errors (2) ===

[browser:js] TypeError (3x, latest 5s ago)
  Cannot read property 'map' of undefined
  → src/components/ProductList.tsx:42:15
  page: http://localhost:3000/dashboard

[proxy:http] 500 Internal Server Error (1x, 12s ago)
  GET /api/products → "database connection refused"

The AI immediately sees both errors and their relationship. The database connection failure causes the /api/products endpoint to return a 500. The component receives undefined instead of an array of products and throws when it calls .map(). The AI fixes the root cause, not the symptom.

Step-by-Step Setup​

Getting agnt capturing errors takes about 30 seconds.

# 1. Install agnt (Claude Code marketplace)
claude mcp add agnt -- agnt mcp

Once agnt is registered as an MCP server, your AI agent has access to all the tools. From inside your AI coding session:

// 2. Start your dev server
run {script_name: "dev"}

// 3. Start the proxy in front of it
proxy {action: "start", id: "app", target_url: "http://localhost:3000"}

The proxy response includes a listen_addr field with the proxy URL (something like http://localhost:34521). Open that URL in your browser instead of localhost:3000. From this point forward, every error is captured automatically -- no browser extensions, no code changes, no configuration.

// 4. Check for errors at any time
get_errors {}

That is it. Your AI can now see everything that goes wrong in the browser.

What Gets Captured​

agnt collects errors from four distinct sources, giving your AI a complete picture of what went wrong.

JavaScript runtime errors -- TypeError, ReferenceError, SyntaxError, and any uncaught exception. Stack traces are automatically reduced to application code frames, stripping out node_modules, webpack internals, and React scheduler noise. What your AI sees is src/components/ProductList.tsx:42:15, not 20 lines of framework plumbing.

HTTP failures -- Every 4xx and 5xx response flowing through the proxy. The tool captures the HTTP method, URL, status code, and extracts a meaningful error message from the response body. JSON responses are parsed to pull out message, error, or detail fields. HTML error pages are stripped to their text content.

Process output errors -- When running in daemon mode, agnt scans your dev server's stdout and stderr for compile errors, panics, unhandled promise rejections, and framework-specific error patterns. A webpack compilation failure or a Go panic shows up in the same get_errors output as browser-side problems.

Custom application errors -- Your application code can log errors directly via window.__devtool.log("Payment processing failed", "error", {orderId: "abc123"}). These appear alongside automatic captures, giving your AI context that only your application logic knows about.

Querying Errors​

The get_errors tool accepts filters so your AI can focus on what matters.

// Everything -- errors and warnings from all sources
get_errors {}

// Errors only, skip 404 warnings and other noise
get_errors {include_warnings: false}

// Only errors from the last 5 minutes (useful during active debugging)
get_errors {since: "5m"}

// Errors from a specific proxy (when running multiple services)
get_errors {proxy_id: "app"}

// Full JSON output for complex debugging scenarios
get_errors {raw: true, limit: 50}

The default compact format is designed for AI consumption -- grouped by severity, deduplicated, with just enough context to identify each problem. The raw: true flag returns full JSON when your AI needs to do deeper analysis.

Built-in Intelligence​

agnt does not just forward raw errors. It processes them to make AI frontend debugging more effective.

Deduplication -- A useEffect that throws on every render can produce hundreds of identical errors in seconds. agnt merges them into a single entry with a count. Your AI sees TypeError (47x, latest 2s ago) instead of 47 separate stack traces.

Noise filtering -- Favicon 404s, source map request failures, HMR WebSocket disconnects, and other development noise are automatically stripped. Your AI only sees errors that matter.

Stack trace reduction -- Instead of a 20-line trace full of node_modules/react-dom/cjs/react-dom.development.js:12345, agnt extracts the first application code frame: src/components/ProductList.tsx:42:15. Supports JavaScript, Go, and Python stack trace formats.

Error message extraction -- When an API returns {"error": "User not found", "code": "USER_404"}, agnt pulls out "User not found" instead of showing the raw response body. Works with JSON, HTML, and plain text responses.

Severity sorting -- Errors appear before warnings. Within each group, the most recent errors appear first. Your AI addresses the most critical, most recent problems first.

Real-World Example: Debugging a Race Condition​

Consider a search input that fires API calls on every keystroke. The user types "react hooks" quickly, and the results page shows stale data from the "rea" query instead of the full "react hooks" query.

Without agnt, this bug is painful to describe to an AI. "Sometimes the search results are wrong but only when I type fast." With agnt, the AI runs get_errors {} and sees:

=== Errors (1) ===

[browser:js] TypeError (2x, latest 1s ago)
  Cannot read property 'title' of undefined
  → src/components/SearchResults.tsx:28:34
  page: http://localhost:3000/search?q=react+hooks

=== Warnings (3) ===

[proxy:http] 409 Conflict (2x, latest 1s ago)
  GET /api/search?q=rea → "stale request superseded"

[proxy:http] 409 Conflict (1x, latest 2s ago)
  GET /api/search?q=reac → "stale request superseded"

The pattern is immediately clear: the API is correctly rejecting stale requests with 409s, but the frontend is not handling the rejection -- it is treating the 409 response body as valid search results and trying to render undefined items. The AI can now suggest adding a check for the 409 status code in the fetch handler, or implementing request cancellation with AbortController.

No copy-pasting. No "can you show me the network tab." No guessing. The error context tells the whole story.

See Also​