Managing State in AI-Powered Distributed Systems
A few months ago, we shipped an AI-powered feature into a system that had been stable for years.
Everything looked fine. System metrics werewithin normal range and logs didn’t show any errors, but the users kept complaining:
“The responses are not helpful.”
There were no errors nor failures, just incorrect responses. That’s when we realized:
Introducing AI into a system means accepting unpredictability.
AI Systems Architecture
Before looking at state management in AI systems, consider the following system architecture:
Simplified AI System Architecture
Traditional Systems Worked Because State Was Controlled
For years, we had been optimizing the system around statelessness where API requests are independent, services scale horizontally, and failures are isolated.
The assumption was that the same input produces the same output.
This made our system work predictably over the years.
When we introduced AI, this assumption broke. Statelessness wasn’t the case any more
The Moment AI Enters, State...
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