u/CrazyGeek7

Im just getting into SDD. Are there any common problems or hindrances or mistakes that AI can make or I can encounter that I should be looking out for?

I've been experimenting with AI agents and autocomplete platforms for a greenfield FastAPI project. In the first few weeks, it felt incredibly fast. But now that we've scaled to multiple routers, complex Pydantic schemas, and SQLAlchemy models, the structural debt is piling up.

The AI writes code that functions, but it constantly violates our architecture. It'll put complex business logic inside a route handler instead of the service layer, or it'll mess up async database sessions across modules. I find myself spending more time refactoring the structure of what it built than it would have taken to write the logic myself.

Is anyone else hitting this scaling wall where AI utility drops off as codebase complexity grows? How are you keeping your system architecture clean?

I’ve been using Cursor/Claude to help scaffold a medium-sized backend in Go. While the velocity is great for simple handlers or repository functions, I feel like I'm constantly fighting the tool on project structure and idiomatic code.

The AI keeps trying to introduce unnecessary abstractions, mixes up package boundaries, or tries to write things in a way that feels like it's treating Go like Java or TypeScript. It makes the code compile and pass basic tests, but the global project layout quickly starts drifting from standard clean architecture practices unless I manually review and rewrite every single file structure.

For those using AI tools on larger Go codebases, how are you enforcing strict idiomatic guidelines on the model? Or do you find it's better to just write the structural boilerplate yourself and only use AI for isolated unit tests?

We’ve integrated AI coding tools into our backend workflow, and while our throughput is up, our structural coherence is hurting. The tool works great when looking at a single file, but it struggles with global repo state.

It frequently invents implicit model relationships, fabricates column constraints that aren't there, or writes endpoints that deviate slightly from our API spec.

It feels like the context window can't grasp the entire database/routing graph simultaneously.

How are backend teams handling this? Are you guys using strict validation loops, linters, or just spending a ton of time in manual code review beating the generated code back into the proper architecture?