AI-Native Architecture
A multi-part series helping you to understand the AI-Native architecture guidelines. Design applications around what AI actually is, rather than bolt AI onto what you already built. Learn about probabilistic behavior, inference economics, the context supply chain, and model lifecycle.
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From Cloud-Native to AI-Native: The Next Evolution of Enterprise Architecture
The anchor article — the four-properties test that opens the series
Every enterprise says it's adopting AI, the same way every enterprise once said it was in the cloud. Bolting AI features onto an existing system is the new lift-and-shift — the four-properties test for what genuinely AI-native architecture requires instead.
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It Passed the Test. That Doesn't Mean It Works.
Act I, article 1 of 4 — property 1: probabilistic behavior
Every testing, monitoring, and reliability practice you own rests on one assumption: same input, same output. An LLM in the call path removes it. What replaces it is evaluation, thresholds, and verification as an explicit architectural layer with a cost and an owner.
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