u/theov666

1-10 how good are you and your team in keeping architectural decisions in documents? Do you have a process or tool to help you with that?

Beyond prompts and memory files, what's actually working?

Looking for practical approaches, not theoretical ones. What's been effective for your team?

I'm particularly interested in what has actually worked once multiple developers and multiple agents are involved. What breaks down first?

My team has people on Cursor, people on Claude Code, one holdout on Copilot, and a CI job that calls the Anthropic API directly. Every one of them needs to know the same things about our codebase: we don't use an ORM, we extend the existing retrieval module instead of rebuilding, no langchain, etc.

Right now those rules live in:

.cursorrules for the Cursor people

CLAUDE.md for Claude Code

.github/copilot-instructions.md for Copilot

a hardcoded system prompt in the CI script

They drift. Someone updates one, forgets the others, and a week later the AI starts suggesting the thing we explicitly banned because the file that particular tool reads is stale.

Curious how other teams are handling this:

Are you actually maintaining N copies, or have you given up and standardized on one tool?

If you're maintaining N copies, what's your hack — symlinks, a generator script, something else?

Has anyone tried writing rules once in a neutral format and pushing them out? Did it work or was it more trouble than it solved?

Honestly, how often do you check whether the AI is actually following the rules vs. just hoping?

Trying to figure out if this is a "me problem" or a real pattern before I keep building around it.

How are experienced teams preventing architectural drift as AI code generation scales?

We’re seeing a new bottleneck emerge internally:

AI can increase engineering output significantly, but human review capacity does not scale linearly with generated code volume.

Even if developers remain fully accountable for every PR, reviewers now need to validate far more surface area, which makes traditional review workflows increasingly reactive and expensive.

Curious how mature teams are handling this in practice:

• Maintaining architectural consistency across AI-assisted PRs

• Enforcing ADRs, constraints, or project conventions during generation

• Preventing drift before code reaches review

• Scaling review/governance as agent throughput rises

Interested in concrete workflows, tooling, or process changes others are adopting.