Retry logic looks simple.
API fails → retry → success.
That’s how most systems handle failures.
But what if retry itself creates a bigger problem?
So I created a small challenge on VibeCode Arena to test this.
And the results were surprising.
🚨 The Problem
Here’s the basic idea:
- Call an API
- If it fails → retry
- Do this a few times
Simple, right?
But there’s a hidden issue.
⚠️ What Can Go Wrong?
In real-world systems:
• Retry can trigger the same action multiple times
• Duplicate requests can create duplicate orders
• Payments can be processed twice
• No delay → system gets overloaded
And suddenly:
👉 One failure turns into multiple problems
🧠 What I Observed
When AI models tried this challenge:
- Some implemented basic retry logic
- Many ignored duplicate request risk
- Some didn’t add delay between retries
- Very few handled idempotency properly
The code works.
But the system is not safe.
🔥 Try My Challenge
I created this challenge to test real-world backend thinking.
👉 Try it here:
https://vibecodearena.ai/share/d8cd72dc-9d3a-4a34-80b4-2544a86f5c8e
Can you:
- Prevent duplicate actions?
- Add safe retry strategy?
- Handle failures without breaking the system?
💡 What Makes This Interesting
This is not just about retrying.
It’s about:
• Reliability
• Data consistency
• Failure handling
• System design
And this is where most AI solutions struggle.
🎯 Final Thought
Retry is not just about trying again.
It’s about making sure…
👉 The system doesn’t break while trying again.
Try it and tell me 👇
Did AI handle retries safely… or create more problems?
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