Codersarts Blog.

Your product rarely fails on a quiet Tuesday. It fails during the launch, the campaign or the big customer rollout, when the most people are watching. The good news is that the warning signs show up well before the spike arrives. Here are seven signs your MVP won't survive its first traffic spike, how to spot them even if you're not an engineer, and what fixing each one involves.

When your LLM provider goes down, every AI feature built on it goes down too, unless you've planned for it. Outages are only one problem: slowdowns, rate limits and model retirements can hurt just as much. Learn how a well-designed fallback chain keeps your AI product online, the design decisions that matter most, and how to prove it works before you need it.

It's 2 a.m. and your LLM provider starts failing. Within minutes, your AI support agent goes offline. Nothing was wrong with its logic, it just wasn't built for the moment its main dependency disappeared. Here are the nine ways AI agents most often fail after launch, why each happens, and the safeguards that keep them running when real users, real traffic and real providers put them to the test.