Product Analytics for Agents and Users

What is Product Analytics for Agents and Users?

Product Analytics for Agents and Users is the core offering from Kubit, a platform designed to bridge the gap between traditional product analytics and LLM observability. It maps user behavior, intent, and sentiment directly to LLM traces, giving product engineers a complete picture of how people interact with AI agents. Instead of juggling clickstream data, prompt versions, and raw logs across multiple tools, Kubit unifies everything into one actionable workflow. The platform connects agent traces to user activities so you can see exactly why users re-prompt, drop off, or convert—then feed those insights directly into your coding environment.

Who it's for

• AI product engineers who need to debug non-deterministic agent behavior without leaving their editor or drowning in raw JSON traces.
• Product teams building LLM-powered features who want to understand user intent, sentiment, and friction signals behind every agent interaction.
• Engineering leads scaling AI products who require self-service analytics to track token costs, error rates, and retention by cohort without manual data stitching.

Key features

Product Analytics for LLM Observability

Kubit goes beyond simple performance monitoring. It offers advanced, self-service analytics to discover patterns at scale, including token costs and error rates broken down by intent and friction signals. You can explore escalation funnels and follow user retention by cohort before and after agent interactions.

Conversation Intelligence Without Limits

The platform extracts intent, sentiment, and resolution directly from your traces, revealing the root cause of user frustration that normally stays buried in logs. Unlike expensive LLM-as-a-Judge approaches, Kubit enriches 100% of your traces with zero sampling, giving you complete coverage without compromise.

Coding Agent Integration and Self-Healing

Kubit embeds directly into Claude Code and Cursor via MCP, equipping your coding agents with live context and skills to debug and auto-fix issues. Your coding agent instantly sees user intent, friction signals, and exact tool call trajectories—without ever opening a browser tab.

Seamless Integrations

Kubit supports major LLM observability tools like Langfuse, Braintrust, LangSmith, and Arize, plus AI frameworks including Langchain and Vercel. With native OpenTelemetry support, the platform handles the heavy lifting and automatically applies industry best practices.

What stands out

Kubit turns unpredictable AI into a measurable product by feeding exact user intent and friction signals straight into your coding agent.

This is the key differentiator: Kubit doesn't just show you dashboards—it closes the loop between insight and action. Instead of manually piecing together context across six different tabs, your coding agent receives the full picture automatically. The platform's ability to enrich 100% of traces with zero sampling, combined with native MCP integration into Claude Code and Cursor, means debugging and optimization happen where you already work. It's an end-to-end Agent Analytics platform that transforms raw telemetry into self-healing capabilities.

Worth checking out if…

You're building AI agents and tired of context-switching between observability tools, clickstream data, and raw logs. If you need to understand the why behind user frustration—not just the what broke—and want your coding agent to act on those insights automatically, Kubit is worth exploring. It's especially relevant for teams using Langfuse, OTel, Vercel, or Langchain who want to add product-level analytics without replacing their existing stack. Start instantly via OTel, your CDP, or Bring Your Own Warehouse (BYOW), and give your engineering team the context they need to ship reliable AI products.