In 30 seconds
Theo Browne describes changing his approach after an AI model failed to identify or fix a problem. He used the agent to locate relevant code and build a browser-console tool, while bringing his own observations and hypotheses to the investigation.
- 1Change the questionWhen the model could not find or fix the problem, Theo shifted to testing multiple hypotheses.
- 2Build a testing toolHe asked for changes that could be tried in production through console commands and observed immediately.
- 3Divide the workThe agent found code and built tools quickly; Theo contributed the information and theories that guided the work.
Takeaways
- An AI model may help investigate a problem even when it cannot identify the cause itself.
- Theo asked the agent to build a console-accessible tool for testing changes quickly.
- The agent accelerated code discovery and experimentation while Theo supplied the observations and theories.
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Using the model to investigate
Theo describes moving from asking a model to solve a problem to using it to test theories. A custom browser tool lets him change options and observe the effects.
At this point I realized the model was not going to solve the problem for me, or even find it. So I changed my mindset: instead of waiting for an answer, I thought about how to use the model to test as many different debugging theories as efficiently as possible.
I asked it to make changes easy to experiment with in production, ideally through console commands so I could see an immediate difference. It wrote a window binding that created __T3GPU, giving us a custom tool for switching options back and forth while investigating.
The agent could find things in the codebase faster than I could and build custom tools to test my theories better than I could. But I was still the one bringing the actual information to the investigation.
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