Your Distributed System Just Got a Brain. Now What?

Distributed systems were designed around predictable behavior. Retries assume idempotency. State transitions assume determinism. Consistency models assume repeatable outcomes. Then we added AI. Now: - the same input may produce different outputs - retries may change decisions - context mutates state unpredictably - model upgrades alter behavior silently - deterministic workflows depend on probabilistic components Your distributed system just got a brain. And distributed systems don’t tolerate ambiguity. In this session, we explore what really changes when AI becomes part of a distributed architecture. We’ll cover: - how probabilistic inference breaks retry semantics - why idempotency assumptions fail with LLMs - separating state from inference - deterministic checkpoints in AI workflows - replayable execution paths - handling model version drift - designing hybrid architectures where AI proposes - but systems enforce AI doesn’t just add intelligence. It changes the fundamental assumptions of your system design. If you treat AI as just another microservice, your architecture will eventually collapse.