How an Agent Can Help Identify the Fix for a "Bug"

Lizzie Zabojnik

— 7 min read
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How an Agent Can Help Identify the Fix for a "Bug"

Agentic AI is smart, independent AI that can think, plan, and take action on its own. It is changing how quality assurance (QA) testing works in a big way. Instead of simple automated scripts that follow fixed steps, these AI agents act like helpful team members. They look at bug reports, your code, and project tools like Jira or Monday.com. They then figure out problems, suggest fixes, and guide testing. This makes everything faster, with fewer mistakes and better software releases. With agents helping, a team of developers can greatly increase the amount of bug fixes and even new features that they release in an update. They can be doing 2x -3x what they were doing before.

How it All Works

1.Reading Bug Reports from Customer Service

The AI connects to tools like Jira or Monday.com and reads all the new bug reports and customer complaints. It pulls out important details such as how to repeat the problem, what part of the app is broken, and any error messages. A regular person might spend hours going through many tickets to find patterns or duplicates. The AI does this in just minutes, even for hundreds of reports. It quickly spots if the same issue happened before and gives a clear summary right away.

Example with DuckBuddy

  • Imagine a developer working on DuckBuddy, a social media app that helps remote workers find others with similar personalities, jobs, interests, and projects. A user reports: “The Save button on my profile doesn’t work when I try to change my profile picture. I pick a new photo, click Save, but nothing happens and I get an error.” The agent reads this Jira ticket, pulls the steps to repeat the bug, notes it happens on mobile and desktop, and links it to similar past image upload issues. It summarizes everything clearly for the developer in seconds.
  • Without an agent, developers had to manually open each Jira ticket or support message, read through messy descriptions, try to reproduce the bug themselves or get clarification from support, and search for similar old tickets in order to find if what was reported was a trend — often spending hours just to understand one issue clearly.

2.Understanding the Code and Database

If the AI can see your source code or the database layout (or a copy of the codebase for safety reasons), it quickly reads and understands everything related to the bug. It checks every line of code, connections between parts, and how things are structured in your codebase to understand the specific of how your project works. A human developer often has to spend a lot of time re-reading code and jumping between files just to remember how a section works. The AI does all this in seconds or minutes. For example, it might say exactly which function or database table is causing the crash.

  • In the DuckBuddy example, the agent searches the codebase and finds the React component for the profile page (ProfileEdit.jsx) that handles the Save button and image upload. It also locates the Python backend endpoint (/api/profile/update) and the SQL table (user_profiles) that stores profile pictures. It traces how the frontend sends the image to the backend and how the database gets updated.
  • Without an agent, a developer would spend a long time opening many files in their code editor, jumping between the React frontend and Python backend, tracing connections by hand, and trying to remember or redraw how the profile picture upload worked.

3.Suggesting What Needs to Be Fixed

After figuring out the problem, the AI suggests clear ways to fix it. It thinks through different options and picks good ones based on how the rest of the code looks. A developer usually reads the code, thinks of possible fixes, and tries things out by hand. The AI creates ready-to-use suggestions fast, often with explanations.

  • For DuckBuddy, the agent discovers the bug is caused by a missing file type check in the JavaScript React frontend. It suggests a simple code change: adding proper error handling and a correct form data setup in the upload function. On the Python backend, it recommends a small fix to better handle the image file. The suggested code might look like this in React:
  • Without an agent, the developer had to carefully study the broken code, come up with a fix on their own, write the new code from scratch, and test it manually multiple times before it worked and often rewrite, rebuild, and retest many times before they got the right solution.

4.Estimating How Long the Fix Will Take

The AI gives a realistic guess on how much time the fix needs. It looks at how complex the code is, past work from your project tools, and similar fixes done before. People often guess based on experience and sometimes get it wrong because of hidden problems. The AI considers everything and says something like, “This is similar to a fix we did last month that took about 4 days.”

  • For the DuckBuddy profile picture bug, the agent checks past similar image upload fixes, how the code for this feature is built, and estimates: “This is a small-to-medium fix. It should take a developer about 3-5 hours, including testing.”
  • Without an agent, developers made rough guesses based on their memory of past work or asked teammates, which often led to wrong estimates and sometimes missed deadlines. Before the days of agents, teams of developers had to manually estimate time for not just bug but also new features in order to help prioritize what bugs and features got worked on in which sprint.

5.Checking How Risky the Change Is

The AI carefully checks how “sensitive” a fix is — meaning how much it might affect other parts of the app, create security risks, or touch important connections. A person has to manually review many files and think about possible side effects, which is easy to miss something. The AI measures the impact and suggests safer ways if needed.

  • In the DuckBuddy case, the agent rates the fix as low sensitivity. It only touches the profile picture upload (a small part of the profile system), has no major security risks if the image size limit stays in place, and doesn’t affect matching algorithms or community features. It says the change has a small impact radius and is safe to deploy.
  • Without an agent, a developer had to manually check many files, think about what else might break or what else is connected to this feature, review security risks by hand, and possibly worry about hidden problems in other parts of the app depending on the complexity of the bug.

6.Suggesting the Right QA Tests

The AI recommends the best tests to run after the fix. This includes quick smoke tests (basic checks), sanity tests (quick verification), full functional tests, regression tests (to make sure nothing else broke), and end-to-end tests (complete user flows). Humans often make test plans from scratch and might forget some important cases. The AI looks at the bug and past tests, then lists exactly what to check — saving time and making sure coverage is good.

  • For DuckBuddy, the agent suggests:
    • Smoke test: Open profile, try uploading any image, and check if Save works.
    • Functional test: Test different image formats (jpg, png), file sizes, and what happens with no image selected.
    • Regression test: Make sure changing other profile fields (bio, interests) still works.
    • End-to-end test: Full flow from picking a photo → saving → seeing the new picture in the community feed and matches.
  • Without an agent, the developers and QA person had to create a test list from scratch or pull from a library of tests, think of all the possible ways the feature could break, and how it needed to be tested. Then they had to come up with the specific steps to test and manually perform those tests or use a Playwright or Puppeteer script for any that could be automated.

All in all, without an agent, when a user reports something like: “The Save button on my profile doesn’t work when I try to change my profile picture” the developer would slowly go through all the steps above: reading the ticket, digging through code, writing a fix, guessing the time, checking risks, and making test plans. With an agent, it reads the report instantly, finds the exact code in the React JavaScript frontend and Python backend, suggests a quick fix for the missing file handling, estimates 3-5 hours, rates the change as low risk, and lists clear tests like smoke, functional, and end-to-end checks etc.

Remembering the Big Picture and Learning from the Past

These AI agents keep a very detailed memory of the whole software project. They quickly remember what fixed similar bugs before and what recent software updates might have caused the new problem. A human might forget details from older work and have to search old records. The AI connects the dots fast and suggests better fixes that handle both old and new issues. It gets smarter over time as it sees more problems and solutions. It can quickly learn and remember your entire code base and all the specifics of how your product works. It learns and knows how long each fix will take and the risk involved, thus helping teams know how to prioritize bugs and which Sprint to place them in. It has a deep knowlegde of all the QA test and can list exactly what tests are needed based on what code changes were implemented for the bug fix, list how to perform each one and estimate long that whole process will take.

In the end, agentic AI does not replace people in development or QA. It helps them by handling the boring, time-consuming parts so humans can focus on creative ideas and big decisions. Teams using this AI can correctly identify and fix bug reported by customers faster. Consequently it can help release software faster, with fewer bugs, and higher confidence. As these smart agents get even better at working with code and project tools, they will become a normal and powerful part of building great software. Companies that start using them now will have a big advantage in the future.

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