The single source of truth approach makes a lot of sense. Config drift between different AI agents could get messy fast. Curious how well does this hold up in a large monorepo with lots of teams?
How should teams manage shared configuration across AI coding agents?
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It's not a bad idea but for me, each api's file is a little different, my codex file and claude.md have somewhat different rules since their models do different things and have different issues I fix in those files. I have an agents github project that gives a UI to multiple agents and manages files etc, https://www.autolearningagents.com/ which has its own compile / docker install, I guess keeping them in sync is a benefit but I'm not seeing the connection between compiling and validating them, other than hand reading them to make sure all their rules are right, so could you expand on the use case? Like is this mainly for machines with multiple claude / codex installs and/or for one developer?
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Treating agent configuration as compilable source is the right framing. The "one source of truth, multiple native outputs" pattern is exactly how successful infrastructure standards evolve—it's the same logic that made Terraform work across cloud providers.
The drift problem you're solving is real. I've covered enterprise teams managing multiple agentic coding tools, and configuration drift between environments is a constant operational headache. When Cursor rules and Claude Code instructions diverge because someone updated one and not the other, you get inconsistent agent behavior that's hard to debug because nobody suspects the config layer first.
Your validate() step is the piece most similar tools skip. Compiling to native formats is only half the problem—confirming those outputs actually satisfy each platform's spec is what prevents silent failures.
On your questions: generated native files over runtime resolution, for one clear reason—agent behavior needs to be inspectable and versionable in git without requiring the resolution layer to be present or correct at runtime. Debugging "what instructions did the agent actually receive" should never depend on a live compilation step.
For an adapter ecosystem to be useful, the registry needs to handle partial spec compliance gracefully (your "◐" status is a good start) rather than treating every target as binary pass/fail—agent platforms are evolving fast enough that partial support will be the normal state, not the exception.
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I use AI heavily, but not lazily. For me, AI is scaffolding, acceleration, compression. It helps me move from idea to structure faster, but I still care about whether the architecture is clean, whether the pieces can survive pressure, and whether the system can still make sense after a month of changes. Speed matters. Durability matters more.
I care about structure more than appearance. If a system looks fine from the outside but hides drift, duplication, confused ownership, or fake abstractions, I do not trust it. I would rather confront the mess early than inherit it later.
I move fast, but not in the careless way people usually mean. Most of my process starts with instinct. I see a shape, a gap, or a system that feels heavier than it should be. Then I move quickly toward a first version. Not because the first version is the goal, but because reality shows up faster when something exists. Show less
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