Kubit
Optimize Agent Actions with User Behavior
53 followers
Optimize Agent Actions with User Behavior
53 followers
Kubit helps product engineers optimize AI agents based on real user behavior. Connect agent traces directly to user activities to see exactly why users re-prompt, drop off, or convert. Then, feed those insights straight into your coding agent to build AI products that actually stick. Start instantly with seamless integrations via OTel, your CDP, or Bring Your Own Warehouse (BYOW).
This is the 2nd launch from Kubit. View more
Product Analytics for Agents and Users
Launching today
Kubit helps product engineers optimize AI agents with user behavior. Connect agent traces directly to user activities to see exactly why users re-prompt, drop off, or convert. Then, feed those insights straight into your coding agent to build AI products that actually stick.
Start instantly with seamless integrations via OTel, your CDP, or Bring Your Own Warehouse (BYOW).
Free Options
Launch Team / Built With
Kubit
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! 🚀
@alexli_kubit Huge congrats on launching🙌visualizing user-agent funnels to spot exact friction points before a user churns is huge for optimizing conversational flows.
Kubit
@alexli_kubit Thrilled to launch today! The gap between tracking human behavior vs. AI agent workflows has been huge—until now. Really excited to see how this helps both individuals and teams get clear insights to optimize user and agent experiences together!