76% of AI agent deployments fail within 90 days. It's almost never the model. It's almost always the infrastructure that was never planned. Here's what actually breaks and why retrofitting it is so expensive.
That 76% number doesn't surprise me at all. Most teams treat the AI model like the hard part and the infrastructure like an afterthought. But in production, it's reversed — the model usually works fine, it's the error handling, retry logic, rate limiting, and state management around it that breaks.
One thing I've seen help: treating every AI call as inherently unreliable from day one. Design for failure upfront instead of bolting on monitoring after things start crashing. If your architecture assumes the model will always respond correctly and on time, you've already lost.
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That 76% number doesn't surprise me at all. Most teams treat the AI model like the hard part and the infrastructure like an afterthought. But in production, it's reversed — the model usually works fine, it's the error handling, retry logic, rate limiting, and state management around it that breaks.
One thing I've seen help: treating every AI call as inherently unreliable from day one. Design for failure upfront instead of bolting on monitoring after things start crashing. If your architecture assumes the model will always respond correctly and on time, you've already lost.