<p><strong>My AI agent kept fixing the symptom instead of the actual bug</strong></p>
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I noticed that when a coding agent encounters an error, it often focuses on making the current test pass without fully understanding why the failure happened.
One fix can introduce another issue, and after a few iterations, the code becomes harder to reason about than before.
I've started asking the agent to explain the root cause and propose a minimal fix before allowing it to edit anything.
It adds a step, but it also prevents unnecessary changes from piling up.
*Do you ask your coding agent to explain the problem first, or let it debug autonomously?
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Explain first, for anything that is not a typo. Two rules in my project file made the biggest difference. One: no fallbacks or error handling for cases that cannot happen. The most common symptom fix I saw was a new try/catch or a default value that makes the error disappear, and banning those forces the agent back to the question of why the value was wrong in the first place. Two: once the cause is found, it gets written down with the reason in a notes file for that part of the app, at the moment it is learned. The next session reads that file before touching the area, so it does not rediscover the same cause and patch it a different way. Your step costs a minute, and in my experience saves the hour where three patches stack on top of each other.