Extracting feedback from agent conversations is harder than it looks because users don't give explicit feedback — they rephrase their query, which is implicit negative feedback, or they accept the output and move on, which is implicit positive. The signal-to-noise ratio in natural conversation is ~5-10% actionable feedback at best. I'd be interested in how Agnost handles the classification pipeline. Are you using an LLM-as-judge approach (costly but flexible) or a fine-tuned classifier (cheaper but needs retraining)? And how do you handle the cold-start problem where you have no labeled data to train the initial classifier? For enterprise deployments, the integration with existing support workflows (Zendesk, Intercom tickets) is often the deciding factor — can Agnost map agent conversations to existing ticket IDs?