To clarify you are using the "fast brain, slow brain" pattern? Maybe an example would help.
replyBroadly speaking, we see people experiment with this architecture a lot often with a great deal of success. A few other approaches would be an agent orchestrator architecture with an intent recognition agent which routes to different sub-agents.
Obviously there are endless cases possible in production and best approach is to build your evals using that data.
Only solution is to train the issue for the next time.
replyArchitecturally focusing on Episodic memory with feedback system.
This training is retrieved next time when something similar happens
Training is an overkill at this point imo. I have seen agents work quite well with a feedback loop, some tools and prompt optimisation. Are you doing fine-tuning on the models when you say training?
replyNope - just use memory layer with model routing system.
replyhttps://github.com/rush86999/atom/blob/main/docs/EPISODIC_ME...
Memory is usually slow and haven't seen many voice agents atleast leverage it. Are you building in text modality or audio as well?
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