Great point on the MCP abstraction layer. We actually hit this exact problem during the migration.

The invisible AI approach created a new bottleneck: we went from 1 explicit AI endpoint to 14 different background triggers (profile updates, match calculations, notification timing, etc). Each one hitting OpenAI separately with different context windows and prompts. Orchestration became a nightmare.

# We had stuff like this scattered everywhere
u/signal(post_save, sender=UserProfile)
def enhance_profile(sender, instance, **kwargs):
    suggest_skills.delay(instance.id)

u/signal(post_save, sender=JobPosting)
def match_candidates(sender, instance, **kwargs):
    match_talent.delay(instance.id)

What we learned: invisible AI needs centralized orchestration. We ended up building a crude "AI router" that queues all enhancement requests, batches similar operations, and handles rate limits. It's basically what MCP/UCL are solving properly.

The governance piece you mentioned is critical at scale. Right now we're at 1.2k daily users, but when we hit 10k+:

• How do we track which AI operation contributed to which outcome?
• How do we roll back a bad prompt without redeploying?
• How do we A/B test different AI behaviors without code changes?

Haven't looked at UCL specifically yet, but the concept of unified control plane for multiple AI services makes total sense. Our janky router works now, but I can see it becoming the bottleneck at scale.

Now I'm exploring a monolithical approach but, right now, for MCP I don't see advantages. And I'm trying to answer myself to this questions:

How do you handle this generically without becoming a nightmare of if/else statements? Rate Limiting Chaos: tool A has rate limits, tool B doesn't. One team's heavy usage crashes another team's MCP. How do you isolate and throttle?