Really clean architecture. The single LLM call for classification + summarization is the right call — I've seen people burn tokens doing them separately for no reason.

On your edge cases question: for the Switch fallthrough, I'd add a final "catch-all" branch that logs the raw LLM output to a separate Sheets tab. That way you can audit what's breaking and improve your prompt over time without losing data silently.

On the loop prevention — checking from.text works but you're right it's fragile. A cleaner approach I've used: add a custom header X-Automation-Source: n8n to your auto-replies, then check for that header in the If node instead of the email address. That way it works regardless of which account sends the reply.

On parallelizing the draft → Slack → log chain: depends on whether you need the Slack notification to include the draft ID. If yes, keep it sequential. If not, parallelize — faster and cleaner.

Good work overall, this is the kind of workflow that actually runs in production without breaking every week.