That's not a bug. That's a missing layer.
Most AI breaks not because of bad prompts or wrong models. It breaks because one piece got built when eight pieces were needed.
The thing gets built. It works in testing. It runs in production for a while. Then it starts drifting. Wrong answers. Edge case failures. Quality that degrades without anyone noticing.
The prompts look fine. The data looks fine. But something's off and you can't quite put your finger on it.
Here's what's happening: the prompts, the AI generation, the outputs you can see. That's 10% of the system. The other 90% is infrastructure underneath. Data pipelines. Quality checks. Orchestration logic. Feedback loops. When that infrastructure is missing or broken, the visible part stops working reliably.
Every AI system that actually works in production uses the same architecture. Not because complexity is the goal. Because each layer solves a specific problem that makes everything else possible.
8 layers. 178 components. One interconnected system.
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AI systems are built in layers. Each layer builds on the ones below. Start anywhere - every component teaches you something new.
Every step below is a layer. Skip any step and something breaks. Not eventually. Right away.
Ten steps. Eight layers. Skip any of them and something breaks.
This isn't complexity for the sake of it. The parts you can't see are the parts that make it reliable.
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