Debugging Frontend Failures 101

When debugging frontend failures, you generally know what has broken. If the ‘login’ button isn’t working, it’s pretty obvious that something has gone wrong somewhere. 

The challenge comes in identifying why the failure has occurred, and how you’re going to fix it. For modern applications with complex combinations of client-side code, APIs, third-party services, and authentication systems, that’s easier said than done. 

A visible failure in the UI is often the final symptom of a much deeper issue elsewhere in the stack. When debugging frontend failures, your job is to investigate the issue, identify the root cause, and fix it before deployment to production.

How easy this is and how long it takes you will depend on whether you’re testing manually, automating some of your testing with an established framework such as Selenium, Cypress, or Playwright, or using newer AI-powered testing platforms such as Momentic.

Here’s a step-by-step walkthrough of each method and how it differs from the others. 

The ‘Traditional’ Process for Debugging Frontend Failures

By ‘traditional’, we mean ‘not AI-assisted’. Engineering and QA teams have been using this process for decades, either completely manually or with the assistance of old-school automation frameworks. 

Whether a failure is discovered by a human or an automated test, the workflow remains pretty constant: 

1. Identify and Reproduce the Failure

The first step: establishing exactly what happened. You’ll need information on the circumstances surrounding the failure, including: 

• User actions leading to the failure
• Browser and device details
• Environment information
• Screenshots or recordings
• Error messages

This will allow your engineers to reproduce the issue. A consistently reproducible issue can be investigated methodically, whereas an intermittent issue may require significantly more effort to diagnose.

2. Collect Evidence

Is this starting to sound like an episode of Columbo? It should – so embrace your inner 1970s homicide cop and get searching for clues. 

Engineers will analyze a range of data when trying to build a picture of what went wrong. This could include: 

• Browser console output
• Network requests
• Application logs
• Monitoring data
• Session recordings
• Error tracking platforms

At this stage, the goal is not necessarily to find the root cause immediately. Instead, you’re building a picture of what happened before the failure occurred.

For example, a broken checkout flow might initially appear to be a frontend issue but could ultimately be traced back to an API response failure or an authentication problem.

3. Isolate the Cause

Got your evidence? You can begin narrowing things down, ruling suspects out of the investigation and building your case against one or two probable root causes (humor us as we abuse the detective metaphor just a little longer). 

Asking the right questions will help you do this effectively. You might want to consider: 

• Did the expected API response arrive?
• Did the frontend receive valid data?
• Did the application state update correctly?
• Did a recent deployment introduce a regression?
• Did a third-party dependency change behaviour?

This is likely where you’ll spend the bulk of your time when debugging frontend systems. There’s a lot of information to wade through, and it’s up to you to decide which parts of this are relevant and which are just noise.  

4. Verify the Root Cause

Got an idea as to what went wrong? Prove it by running local tests, comparing previous builds, reviewing deployment history, and simulating alternative scenarios. 

You’ll need to convince the jury in your daily engineering standup that the issue genuinely caused the failure rather than simply appearing alongside it, so do your due diligence here. 

5. Implement and Validate the Fix

Implement the fix, rerun tests, and repeat the original workflow. This verifies that the failure no longer occurs, the fix behaves correctly across supported environments, and that the fix hasn’t introduced any new regressions that need addressing. 

At this point, you’re done! Put your feet up (or move on to the next of ten more bugs you need to fix this development round). 

So, Where Do Test Automation Frameworks Fit Into The Process? 

Automation improves detection speed and coverage because you don’t have to wait for slow manual rounds of testing. They will not help with the ‘whys’ of the investigation any more than manual testing. For example, an automated test might report: 

• Element not found
• Assertion failed
• Request timed out
• Unexpected page state

So, while an automated test may detect a failure quicker than equivalent manual processes, engineers will still need to go through the process above to resolve the bug. Automation won’t reproduce the issue, gather and analyze evidence, or determine the root cause for you. 

As a result, engineers still spend significant time collecting additional information before they can begin diagnosing the issue.

The Process for Debugging Frontend Failures with AI-Powered Tools

Where AI tools differ from traditional frontend debugging processes is their ability to reduce time spent on investigation, alongside speeding up detection processes. If you want to really accelerate your release cycle, this is where you should be looking. 

There’s no change in objective – you’re still finding and fixing the root cause of the error – but with AI tools, you’ll be able to do that significantly faster. Here’s what the process looks like. 

1. Detect the Failure

As with traditional automation, the process begins when a test identifies an issue. AI-powered systems can capture more contextual information during execution, so you’re already starting off better informed than you would be at this stage of a traditional frontend debugging process. 

2. Let the AI Collect the Evidence

This is where the differences really start to show. 

When you’re debugging frontend failures using the traditional methods, you’ll need to manually review logs, screenshots, network traces, and test output to determine why a test failed. Agentic AI systems, on the other hand, can analyze many of these signals autonomously and present you with a likely cause of failure. 

For example, an agentic AI tool might flag: 

• Unexpected UI changes 
• Missing page elements
• Behavioral deviations
• State inconsistencies
• Workflow interruptions

So, you save the time you would have spent trawling through reams of information and are directed straight to the bits that really matter to your investigation. 

3. Prioritize Likely Causes

AI testing platforms go beyond identifying the relevant information for your failure investigation – they also help you narrow down the root cause of your defect. 

So, rather than having to examine dozens of possible explanations, AI-driven analytics tools can suggest which events are most likely to have caused the failure. The AI isn’t doing the work for you, exactly, but it does substantially reduce the amount of exploratory work you need to do to arrive at a likely answer. 

Think of it like a signpost. The AI shows you where to start looking – your job is to verify which of the AI’s suggested explanations are correct. 

4. Verify the Root Cause

The golden rule: AI suggests reasons for failure. Your engineers confirm which of these is the root cause. 

Human involvement is still essential for visibility, accountability, and for improving software quality over time. If your engineers know what went wrong this time, they can avoid it in the next development round. 

You’ll still need to confirm the root cause of the issue by yourself – due diligence around running local tests, comparing previous builds, reviewing deployment history, and simulating alternative scenarios still applies. But you have two key advantages over the traditional method, which will save you a lot of time: 

1. You’re working from a much narrower list of possible causes, thanks to AI involvement in the previous step
2. Tests are easier and faster to rerun, thanks to AI-native test creation and maintenance tools such as natural language test creation and self-healing tests

5. Implement and Validate the Fix

The final stages of the process are mostly unchanged. You still have to implement the fix and verify that the failure is resolved and that you haven’t broken anything else in the meantime. 

The Key Difference Between Debugging Frontend Failures With AI and Without AI

It’s not finding the failures themselves – most engineering teams are pretty good at that. If something breaks, you know about it. 

Instead, it’s about streamlining time-hungry investigative processes surrounding why the defect occurred. The amount of manual effort required between detection and diagnosis can be substantially reduced.

A comparison of the debugging process with and without AI illustrates this:

This is particularly valuable for large applications where failures may involve multiple systems and generate significant amounts of diagnostic data.

Debug Frontend Failures Faster With Momentic

"We couldn’t reasonably scale with manual testing, and heavy scripted automation doesn’t solve the real problem – it just shifts the burden. Momentic was the only solution that helped us eliminate that burden.”
Glavin Wiechert (Founding AI Engineer, Coframe)

Coframe realized that their manual testing processes were creating bottlenecks that prevented their team from performing at their full potential. 

After implementing Momentic, they now catch 80% of critical UI errors before deployment, all while speeding up end-to-end test creation by 70%.