Product Analytics by Kubit - Optimize Agent Actions with User Behavior.

Hey Product Hunt! 👋 I’m Alex, founder and CEO of Kubit.

When an AI feature fails, existing observability tools tell you what the agent did, and traditional analytics tell you if the user left. Neither tells you why because they don't talk to each other. You're left toggling between tabs, manually matching AI execution logs to front-end user sessions just to figure out what broke the experience.

We experienced this frustration first hand when building our own AI features. So we built Kubit to bridge this exact gap: a unified product analytics platform designed for both agents and users.

With Kubit, you can:

• Connect Agent Traces to User Behavior: Link backend agent traces directly to user actions to see why users re-prompt, abandon a flow, or convert.

• Tie Agent Performance to User Outcomes: Correlate P95 latency, token usage, and model costs directly to core metrics like DAU, retention, and LTV.

• Track User-Agent Funnels: Pinpoint the exact step where an agent’s hallucination or failed tool call disrupts a conversion funnel.

• Map AI User Journeys: Track re-prompts, rage clicks, user intent, and sentiment to uncover hidden UX dead-ends that standard APMs miss.

• Build Granular Cross-Domain Cohorts: Segment users using complex conditions that combine both backend agent interactions and front-end user behavior.

• Headless for Coding Agents: Leverage MCP and custom Skills for headless analytics designed for developer-facing AI tools.

Easy Integration & Flexible Data Architecture

• Quick Setup: Connect Kubit directly to your existing OpenTelemetry (OTel) infrastructure or CDP in minutes.

• Zero-Copy / BYOW: Prefer to keep data in your own stack? Our Bring Your Own Warehouse (BYOW) architecture ensures top-tier security, compliance, and control.

We built Kubit to help product and AI engineers optimize agent performance alongside user behavior to create AI products that actually stick. Try it out for free today at kubit.ai.

We’d love your feedback! How are you currently tackling agent observability and user analytics? Drop your thoughts, questions, or feature requests in the comments below, join the conversion, or reach out directly at alex@kubit.ai.

Happy building! 🚀

I talk to teams building AI features every week, and it's always the same story. They can tell you what the agent did. They can tell you if the user bailed. What they can't tell you is why, because those two things live in separate tools and nobody's connected them.

That's the whole reason Kubit exists. Agent traces and user behavior, same place, so you can actually trace a bad tool call to the drop off it caused instead of guessing.

Would mean a lot if you checked it out and gave us an upvote. And if you're wrestling with this problem yourself, tell me how, I'm curious.

Interesting project. We're currently doing this manually: we track the prompts, and whenever a user is dissatisfied, we pass the case to the team for improvement. We do it manually because we want to understand the reasons behind the AI's poor-quality responses, rather than simply optimizing based on user feedback.

the "did a hallucination cause a rage click, or did latency cause the drop off" example is the interesting claim here, but that's attribution, not just correlation, and attribution is the hard part. a user can rage click right after a bad tool call for a totally unrelated reason, their wifi hiccuped, they got distracted, the UI was slow for some other reason that session. if the tool tells an engineer "this specific tool call caused this specific churn" and it's actually just two things that happened near each other in time, that's worse than no insight at all because now someone's confidently fixing the wrong thing. how does Kubit distinguish a real causal link from two events that just happened to land in the same session?