Building AI Products–Part I: Back-End Architecture

Many of the APIs or LLM extensions provided by AI companies are written by ML engineers that do not have Phil's decades of experience in distributed systems, databases and networking. That is evident after reading this article; the first time I've seen a coherent discussion of the tools and tradeoffs when building agentic systems.

I've struggled to actually build something useful with the "agentic" systems and tools out there (and I've tried a lot). Deep down I've felt intimidated by the dozens of new terms the docs use, and after reflection, those tech marketing pieces give the vibe that they are written primarily by AI and told to be colorful and not clear and precise. These solutions from billion dollar valued companies must to present "brand new" ideas to justify their valuations. We should know better: everything builds on the shoulders of decades of research and discovery. If you see something flying high in the clouds (and not standing on the shoulders of giants), it is sure to fall back to earth soon.

A great read. I'm very excited about Outropy.

> Agents naturally align with OOP principles: they maintain encapsulated state (their memory), expose methods (their tools and decision-making capabilities via inference pipelines), and communicate through message passing

it does sound like a service (memory=db,methods+messages=api). it is just the level of isolation/deployment you need

UPD: also, how come your services share database layer (?), maybe problems in scaling are not due to Agents at all? do you have scaling issues even without agents? would not be surprised! classic rule form Amazon 2002 API mandate by Bezos "no shared db between services. all communication happens over exposed interfaces and over network".

This does not mean that agents can not run in microservices, just that is is not 1:1 between agent and a microservice.