The more I used AI coding tools, the more I realized the real problem wasn't the AI.
First of all, thank you to everyone who commented on my previous post. Reading different perspectives from founders and engineers has been incredibly valuable.
One comment kept coming back in different forms:
"AI is great until it loses context."
That resonated with me because I've experienced the same thing.
When I start a new chat with an AI coding assistant, it doesn't know anything about my project.
It doesn't know:
Why I chose a microservices architecture instead of a modular monolith.
Which security principles are non-negotiable.
Why certain technical decisions were made six months ago.
Which conventions my team has already agreed on.
How different services and domains relate to each other.
So what do we do?
We write longer prompts.
We paste documentation into the conversation.
We upload architecture diagrams.
We remind the AI of the same rules over and over again.
Eventually, the conversation becomes less about solving problems and more about rebuilding context.
That doesn't feel like the future of software engineering.
It feels like a workaround.
This is one of the core assumptions behind NAEOS.
Instead of treating engineering knowledge as something humans read and AI occasionally references, what if we treated it as a structured system that AI could understand from the beginning?
Imagine every AI agent joining a project with access to:
The architecture.
The engineering standards.
The decision history.
The documentation.
The security policies.
The team's workflows.
Not as scattered documents.
But as a shared engineering foundation.
I'm still exploring whether this idea can work at scale, but the more I build, the more convinced I become that context—not code generation—is the next big challenge in AI-assisted software development.
I'd love to hear how others are solving this today.
How do you keep AI aligned with your project's engineering decisions over weeks or months of development?