How I Solved a Major Bug by Teaming Up Two AI Tools

Combining the specialized strengths of two distinct AI tools proved to be the breakthrough needed to diagnose and resolve a complex, system-crippling bug that neither could solve alone. This experience highlights a powerful emerging workflow where OpenAI’s Codex and ChatGPT Deep Research function as complementary team members, each bringing a unique capability to the table. The journey from discovery to solution underscores that while AI can dramatically accelerate development, human oversight and creative problem-solving remain indispensable.

That initial moment of confusion is familiar to any developer. You notice something slightly off, a minor hiccup that doesn’t immediately scream “catastrophe.” For me, it was a strange, 20-second freeze that only happened the first time I accessed my WordPress development dashboard each morning. After that initial lag, everything would run smoothly. It was easy to dismiss it as a quirk of the local environment, a phantom glitch not worth a major investigation.

My first attempt at a diagnosis involved Codex. I explained the intermittent freezing, but without a clear, reproducible test case, my guidance was vague. Codex, acting as a pair programmer, couldn’t pinpoint the issue. I even had it build a custom diagnostic platform to log every hook, call, and delay during WordPress startup. The telemetry came back clean, offering no clues. The problem’s elusive nature, appearing only once per day, made it incredibly difficult to capture.

I eventually convinced myself it wasn’t a serious bug and moved on to other tasks, like creating tutorial videos for my new plugins. This led to a critical turning point. To demo a site analysis tool with real data, I installed the new plugin suite on my live, user-facing server. The result was immediate and disastrous. The entire site, both the admin area and the public front end, became unusably slow, with minute-long delays on every click.

This was no minor development quirk; it was a full-blown, site-breaking bug. I had to manually delete the plugin via my hosting provider’s file manager to restore the site. The severity was now undeniable, and the source was clear: my AI-generated code update.

Returning to Codex with this new, concrete information yielded little progress. Codex excels at focused, granular coding tasks but struggles with large-scale, systemic debugging. It’s like a brilliant short-order cook overwhelmed by a massive, complex catering order. I provided numerous prompts, but each time, Codex would “think” for several minutes only to return a non-functional or irrelevant suggestion. I was facing the prospect of weeks of manual code archaeology, which would negate the productivity gains the AI had provided.

Frustrated but determined, I changed tactics and enlisted ChatGPT Deep Research. This tool is built for big-picture analysis. I granted it access to my project’s GitHub repository, explained the freezing problem, and set it to work. Its initial report was a red herring, blaming stable, long-shipped parts of my codebase. The key was to direct its analysis. I uploaded the distribution zip for the stable, previous version (3.2) and instructed Deep Research to compare it with the problematic new version (4.0), focusing only on the changes.

This focused approach worked. About thirty minutes later, it identified the culprit: a routine that checked the status of a `robots.txt` file. This check was supposed to run once to initialize certain features. Instead, the new code was executing it on every single page load. On a low-traffic development site, the impact was a brief pause. On a live server, it created a traffic jam of PHP processes, effectively freezing the site.

Armed with this precise diagnosis from Deep Research, I went back to Codex. Now, with a narrow, well-defined problem, Codex sprang into action. We discussed the solution, and I instructed it to modify the code so the status was checked only once at startup, with an optional admin button to manually trigger a recheck. Codex provided a clean, working build. After deploying it to my live server, the freezing issue vanished completely.

This process felt like managing a specialized team. Deep Research was the senior systems analyst who could sift through mountains of data to find the root cause. Once the problem was identified, Codex, the proficient implementation engineer, quickly wrote the precise fix.

It’s crucial to recognize that the AIs didn’t solve this autonomously. Human intuition was required at every stage. I was the one who discovered the bug’s severe manifestation through real-world testing. I was the one who conceived the strategy of comparing old and new code. I was the one who bridged the gap between the diagnostic tool and the coding tool. The initial coding may have been done in days, but the subsequent testing, debugging, and productization represent a significant ongoing human effort.

This experience suggests a potent future workflow: leveraging different AI models for their specific strengths. Have you encountered a situation where a bug only revealed itself under specific, real-world conditions? Do you think a multi-tool AI approach could become a standard part of your debugging process?

(Source: ZDNET)