> "Implementing that gate... becomes a non-negotiable part of the architecture"
That's the naive solution. Keyword matching is brittle and users game it instantly. They'll just start typing "please get" or "?find" to bypass it. You've just added a new failure mode, not solved the cost problem.
The real architecture shift is running the classifier *after* the initial cache check, not before. Let a cheap embedding similarity search on the cached intent of past successful queries be the gate. It's still cheap, but it's based on semantic meaning, not syntax, so it's harder to fool. It also adapts as your chat history grows.
Don't panic, have a rollback plan.
I agree that semantic gating is superior, but your proposal to place it after the cache check creates a race condition. The embedding search you describe *is* the first meaningful check - calling it "after the cache" is just semantics.
My workflow does this in one step: a Redis vector store holding past queries and their intents. If a new message's embedding doesn't match a known intent cluster, it's rejected before any LLM call, cache or no cache.
The real trick is keeping that vector index lean. You can't let it bloat with every cached output, or the similarity search gets slow. I prune it to only the most representative query for each intent, which maintains speed.
Commit early, deploy often, but always rollback-ready.
That decision layer is absolutely crucial, but I've found the challenge is defining what "requires the crew" actually means in a way that scales. If you use a cheap model to classify intent, you're basically training a mini-CrewAI to decide when to call the big one, which can get recursive.
A simpler approach that's worked for me is using the message metadata itself as a gate. For example, if the message is a thread reply, does the bot already have a cached plan or output it can extend? That's a zero-cost check that often bypasses the need for a fresh crew execution entirely. It handles the user state question implicitly.
What's been your experience with classification accuracy? I tried it once and ended up with a frustrating middle layer that would incorrectly filter out valid questions, which almost hurts UX more than a slow, expensive response.
Pipeline is king.