Recently we had a brief problem on Trip where the site became unstable and temporarily crashed. What followed turned into an interesting example of how AI can help diagnose tricky technical issues.

The problem started when we noticed that some of our servers were repeatedly failing. At first, the cause wasn’t obvious. The system had been running smoothly, and the usual monitoring tools didn’t clearly show what was going wrong.

One of our developers downloaded the detailed system logs and tried something a little different. Instead of manually combing through thousands of lines of information, he asked Claude (an AI system) to analyse the logs and the relevant code.

Claude suggested a possible explanation: Under certain circumstances, the software could accidentally try to send two replies to the same request.

In web systems, each request must receive exactly one response. Once the system sends that reply, the connection is effectively finished. If the software tries to send another one, the server throws an error because the conversation is already closed.

Normally this wouldn’t happen often. But if it occurs repeatedly, those errors can accumulate and cause servers to fail.

And that’s exactly what happened.

It appears the issue was triggered by Google’s web crawler, which was sending a variety of unusual requests to the site. Those requests exposed a hidden bug in our code that had probably been sitting quietly there for some time.

Once the problem was identified, the fix was straightforward and has now been deployed.

The interesting part of the story is how quickly the issue was diagnosed. Debugging problems like this can often take hours of searching through logs and code. In this case, AI helped highlight the likely cause almost immediately.

It’s a small example of how AI is starting to act as a useful assistant for engineers, helping identify problems faster and keeping services running smoothly.