10-Layer Architecture
Staff-level 10-layer architecture for AI-native systems: from infrastructure to user experience, with production examples.
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Staff-Level Architecture: 10 Layers
What separates an “LLM app” from a production-grade autonomous AI platform. Each layer is a design decision interviewers probe at senior and staff level — and each carries a slice of a real budget.
A demo is one API call you can hold in your head. A platform is ten layers, each absorbing a class of failure the demo never sees, and each owning a line in a real budget. Ground every layer in the canonical support-agent: ~50K queries/day, ~$0.0033 per query, ~$4.9K/month, scaling toward 12 teams, 60 engineers, and model spend north of $60K/year. This rewrite walks all ten layers with the numbers, the incident each one prevents, and the three decisions worth writing down.
A demo is one API call. A platform is ten layers, each absorbing a class of failure the demo ignores. You do not have to build all ten for every problem — but at staff level you are expected to know they exist and to say which one you are working in at any moment. That running commentary (“this is the gateway layer, so here I’d add fallback…”) is the clearest signal that you hold the whole system in your head.