Why AI Applications Are Becoming Distributed Systems

Description and use

<p>AI applications used to be relatively simple.</p> <p>A user sent a prompt. An application sent that prompt to a model. The model returned an answer. The application displayed it.</p> <p>That architecture is changing quickly.</p> <p>Modern AI applications increasingly retrieve information, call external APIs, execute tools, interact with databases, invoke multiple models, run background tasks, maintain state, and sometimes delegate work to other AI agents.</p> <p>At that point, you are no longer building a simple application with an AI feature.</p> <p>You are building a distributed system.</

Problem

<p>AI applications used to be relatively simple.</p> <p>A user sent a prompt. An application sent that prompt to a model. The model returned an answer. The application displayed it.</p> <p>That architecture is changing quickly.</p> <p>Modern AI applications increasingly retrieve information, call external APIs, execute tools, interact with databases, invoke multiple models, run background tasks, maintain state, and sometimes delegate work to other AI agents.</p> <p>At that point, you are no longer building a simple application with an AI feature.</p> <p>You are building a distributed system.</

Outcome

<p>AI applications used to be relatively simple.</p> <p>A user sent a prompt. An application sent that prompt to a model. The model returned an answer. The application displayed it.</p> <p>That architecture is changing quickly.</p> <p>Modern AI applications increasingly retrieve information, call external APIs, execute tools, interact with databases, invoke multiple models, run background tasks, maintain state, and sometimes delegate work to other AI agents.</p> <p>At that point, you are no longer building a simple application with an AI feature.</p> <p>You are building a distributed system.</

Evidence

Why AI Applications Are Becoming Distributed Systems

Source