Your LLM didn't fail. Your application trusted it too much.

A developer building AI applications identified a critical reliability gap: LLM outputs that appear correct but contain subtle errors like missing fields or violated business rules. To address this, they are building Linden, an AI reliability layer that evaluates responses before production use, with actions including ALLOW, WARN, REGENERATE, and BLOCK.

A few months ago, I noticed something frustrating while building AI applications: The model response looked perfect. The JSON was valid. The schema matched. The API returned 200. But the output was still wrong. A field was missing. A value didn't make sense. A business rule was violated. The dangerous failures are not the obvious ones. It's the responses that look correct enough to pass through your system. That's the gap I started exploring: How do we decide when an AI output is actually safe to use? Most teams today add: custom validation code retry loops manual checks another LLM to review the output But reliability shouldn't be an afterthought added to every AI workflow. I'm building Linden, an AI reliability layer designed to sit between LLM outputs and production systems. The idea: Every AI response gets evaluated before your application trusts it. ALLOW → continue WARN → continue with caution REGENERATE → attempt recovery BLOCK → prevent the output The goal isn't to make AI perfect. The goal is to make AI systems safer to build with. I'd love to hear from engineers: What's the most painful LLM failure you've dealt with in production?