The broader concern:

The AI assistant doesn't appear to have visibility into how the platform's own infrastructure works under the hood. When the AI is building code that runs on Base44's infrastructure — automations, backend functions, scheduled jobs — it needs to understand how that infrastructure behaves. Specifically:

• How are function deployments versioned and cached?

• How do automations bind to specific function runtimes, and when do they pick up new versions?

• Why can manual invocations and scheduled invocations execute different versions of the same function?

• What is the correct way to force a full redeploy that propagates to the automation runtime?

Without this knowledge, the AI ends up in a loop of editing code, declaring the fix successful based on a manual test, then watching the automation fail again for reasons outside the code itself. We went through several cycles of this before identifying the root cause as a platform deployment issue rather than a code logic issue.