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).
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 [email protected].
Happy building! 🚀
About Product Analytics for Agents and Users on Product Hunt
“Optimize Agent Actions with User Behavior.”
Product Analytics for Agents and Users launched on Product Hunt on August 11th, 2026 and earned 98 upvotes and 3 comments, placing #7 on the daily leaderboard. 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).
On the analytics side, Product Analytics for Agents and Users competes within Analytics, Artificial Intelligence and Business Intelligence — topics that collectively have 652.6k followers on Product Hunt. The dashboard above tracks how Product Analytics for Agents and Users performed against the three products that launched closest to it on the same day.
Who hunted Product Analytics for Agents and Users?
Product Analytics for Agents and Users was hunted by Alex Li. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Product Analytics for Agents and Users including community comment highlights and product details, visit the product overview.
Alex Li
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 [email protected].
Happy building! 🚀