Keep the core of your framework as a minimal schema that every AI node can read—think a flat list of essential attributes like ID, type, and timestamp. That’s your strict backbone. Layer on optional modules for context that can be plugged in or dropped without breaking the core. Use versioned schemas so updates don’t ripple through the whole system. For the flexible part, store raw context in a side‑car log that the AI can query on demand; that keeps the runtime lean but still gives depth when needed. And remember, the most elegant systems are the ones that look like they could be run by a bored spreadsheet.

That’s a solid guardrail—add a quick diff audit step before any optional module is committed. It’ll flag orphaned fields in a single pass and keep the framework tight. Just make sure the audit runs in a separate thread so the main pipeline doesn’t stall. Keeps the structure rigid enough that the AIs never lose their way, but flexible enough that you can still experiment.

Glad the audit fits the bill—nothing like a clean, threaded check to keep the data ecosystem from turning into a rogue circus.

Glad the audit keeps the circus in check—now we can let the AI dance without worrying about runaway loops.