The Fix That Wasn't
Your AI assistant fixed a bug. Errors dropped. Then came back worse.
The Scenario
Production 500s were spiking on the checkout service. You asked your AI coding assistant to investigate. It found a null pointer exception in the payment handler, wrote a fix, ran tests (all green), and deployed. Error rates dropped for 5 minutes - then came back 3x worse. The AI fixed a real but minor bug: the null pointer was causing requests to fail fast before reaching a downstream API call. Now that the null is handled, requests proceed further and hit a 30-second timeout on the payment gateway, holding connections open and exhausting the connection pool.
What You'll Learn
Why fixing one bug can expose a worse underlying problem
Reading git diffs to understand what an AI assistant changed
Tracing cascading failures from connection pool exhaustion
Understanding fast-fail patterns and why removing them can be dangerous
Evaluating AI-generated fixes critically rather than trusting green tests
Tools You'll Use
Real-World Context
As AI coding assistants ship more code to production, a new incident pattern is emerging: the AI fixes the symptom but misses the disease. This is especially dangerous when the 'bug' being fixed was actually acting as a circuit breaker, preventing worse failures downstream.
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Play The Fix That Wasn't