Imagine hiring a brilliant software engineer who suffers from complete amnesia every time they blink.
Every time you ask them a question, you have to hand them their entire employment history, the codebase documentation, your style guide, and a summary of every conversation you’ve ever had with them. They process the information, give you a great answer, and then—blink—it’s all gone.
This is the exhausting reality of stateless AI applications.
Most developers building with Large Language Models (LLMs) today are stuck in this stateless paradigm. They write clever prompts, wrap them in an API call, and rely on the application layer to aggressively feed the entire chat history back into the context window with every new turn. It’s expensive, it’s inefficient, and it places a hard ceiling on how smart an agent can actually become.
To build truly autonomous, adaptive, and personalized AI systems, we must cross the chasm from stateless interactions to stateful agents.
