Architecting AI Systems Before Features Exist
Most AI failures aren’t model failures.
They’re system failures caused by optimizing for capability before control.
Before features, we focus on:
- isolating inference from orchestration
- defining behavior under uncertainty
- bounding system responses
- designing explicit override paths
Accuracy metrics don’t capture stability.
Highly sensitive systems react to noise.
Stable systems absorb it.
In production, consistency matters more than precision.
That consistency comes from architecture, not training.
Models will change.
Policies will evolve.
Usage will drift.
Systems designed for replacement survive those changes.
Systems designed for demos don’t.
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