The Origin Story (The "Why")

Hey Product Hunt! Alex here. 👋

A few months ago, while building our own AI features, we kept hitting a wall.

Our APM/observability tools (LangSmith, Langfuse) would show us that an agent executed a 4-step tool chain with 800 tokens. Great. Our analytics would show us the user dropped off 12 seconds later.

The problem? Neither tool could tell us why. We were stuck in a multi-tab nightmare, manually cross-referencing timestamps on backend LLM logs with front-end session replays just to figure out why a user gave up.

That frustration is why we rebuilt Kubit. We realized that in the age of AI, agent execution is the user experience. You can't analyze them in silos anymore.

I'd love to know: How are you currently bridging the gap between your LLM traces and your user retention metrics?