🐛 QA is Dead (Long Live the Agent): How Cursor's 'Bug Bot' Fixes Code While You Sleep

Source: Dev.to

Introduction

Let’s be honest: The worst part of being a software engineer isn’t writing code. It’s debugging it.

We’ve all been there. A user reports a bug: “The save button doesn’t work.” No logs, no steps to reproduce, no screenshots. You spend hours trying to recreate a state that exists only on one specific machine.

What if you could outsource that misery?

Cursor, the AI‑powered code editor, recently announced an internal tool called Bug Bot. It aims to automate the most painful part of debugging: reproducing the bug.

📉 The “Reproduction” Hell

In traditional development, fixing a bug is roughly 10 % coding and 90 % reproduction. If you can’t reproduce it, you can’t fix it. Large language models (LLMs) have struggled here—they can suggest possible causes but don’t actually run code to verify them.

🤖 Enter the Agent: How Bug Bot Works

Cursor’s Bug Bot is an autonomous agent, not a chatbot. It reads code and executes it. The workflow, described in their engineering deep dive, consists of three main phases.

1. The Context Hunt (RAG on Steroids)

When a bug report arrives, the bot scans the entire codebase (using Retrieval‑Augmented Generation) to map dependencies, API calls, and state‑management logic related to the issue.

2. The “Scientist” Loop (The Killer Feature)

The bot generates a reproduction script—a small test case (e.g., a Python script or a Jest test) that attempts to trigger the bug. It then runs the script:

• If the script fails (no bug found): The bot analyzes the error, rewrites the script, and tries again.
• If the script succeeds (bug found): The bot flags the issue as “Reproduced.”

It iterates until it can reliably reproduce the failure.

3. The Fix

Once a reproducible test consistently fails, an LLM can modify the source code until the test passes, effectively producing a fix.

🧠 Why This Is “Viral” Tech

Bug Bot bridges the gap between generation and execution:

• Eyes: It reads the repository.
• Hands: It writes files and runs terminal commands.
• Brain: It analyzes its own output and adjusts its actions.

These feedback loops embody agentic engineering, moving beyond “fire‑and‑forget” code generation.

🛠️ The Architecture of a Bug Bot

If you wanted to build a similar system, the high‑level components are:

• Trigger: A GitHub Issue, Linear ticket, or other bug report.
• Planner: An LLM that decides where to look in the codebase.
• Executor: A sandboxed environment (e.g., a Docker container) where the agent can safely run tests or scripts.
• Evaluator: Logic that inspects terminal output to determine success or the need for a retry.

🚀 What This Means for Your Job

Manual QA isn’t disappearing, but its traditional form is on life support. Developers are shifting from “writing logic” to designing systems that write logic. QA engineers may soon focus on building and supervising the agents that perform the repetitive testing tasks.

🔮 The Verdict

Bug Bot offers a glimpse of software development in 2026. Instead of waking up to a Jira ticket that says “Fix this,” you might receive a pull request from a bot:

“I found the bug, reproduced it with this test case, and here is the fix. Please review.”

Are you ready for your AI co‑worker?

🗣️ Discussion

Would you trust an AI to close Jira tickets for you? Share your thoughts in the comments below!