🦞I Let an AI Debug My Production Bug… and It Was Right

I spent 3 hours debugging a bug.

The AI solved it in 30 seconds.

That’s when I realized:

👉 We’re not debugging the same way anymore.

⚠️ The Bug That Didn’t Make Sense

I was working on a real feature:

Trading partner selection
Auto-populated buyer details
React + async API

Everything looked correct:

API response ✅
Backend logic ✅
No errors ❌

But…

👉 The dropdown randomly stopped working.

Not always.
Not consistently.
Just enough to be frustrating.

🧠 The Breaking Point

After hours of:

checking logs
watching network calls
re-reading code

I stopped and thought:

What if I could get help debugging this like how a senior developer would approach it?

So I built a small AI agent using OpenClaw to analyze the issue.

🤖 The Experiment: AI Debugging Agent

Instead of guessing, I gave the AI structured input.

Input:

{
  "issue": "Dropdown disabled after selecting trading partner",
  "apiResponse": "valid",
  "state": {
    "tradingPartnerIdNumber": "exists",
    "displayList": []
  },
  "effectDependencies": ["tradingPartnerIdNumber"]
}

🧠 AI Output
Possible issue detected:

displayList is empty when useEffect runs.

This suggests a race condition.

Suggested fix:
Include displayList in dependencies.

🧩 What Was Actually Happening

The issue was a race condition.

The effect was running too early—before the data was ready.

Flow:
User selects trading partner
↓
useEffect runs immediately
↓
displayList not ready
↓
autofill fails
↓
UI becomes inconsistent

🛠️ The Fix

❌ Before

useEffect(() => {
  if (tradingPartnerIdNumber) {
    autofillBuyerDetails();
  }
}, [tradingPartnerIdNumber]);

✅ After

useEffect(() => {
  if (tradingPartnerIdNumber && displayList.length > 0) {
    autofillBuyerDetails();
  }
}, [tradingPartnerIdNumber, displayList]);

📊 Result
Dropdown works consistently
No more random failures
No backend changes needed

💡 What This Taught Me

1. Most Bugs Are Timing Problems

Not logic. Not syntax. Timing.

1. Debugging Is Pattern Recognition

And AI is surprisingly good at spotting patterns quickly.

1. Structured Debugging Works

Instead of guessing, breaking problems into inputs + analysis leads to faster solutions.

⚔️ Why OpenClaw Helped

Using OpenClaw made it possible to:

structure debugging input clearly
reuse the same approach for future issues
treat debugging like a repeatable system

Instead of a one-time answer, it becomes a workflow.

🚀 Final Thought

This started as a small UI bug.

But it changed how I think about debugging.

Instead of trial and error, we can move toward:

👉 systematic, assisted debugging

And this is probably just the beginning.

Comments

[Siti Aisyah Mat Zainal]

Curious : what’s the most frustrating bug you’ve debugged?

I’m thinking to feed more real cases into this AI and see what happens 👀