Introduce Kubit
Kubit is a product analytics platform that combines Agent Observability, Conversation Intelligence (intent, sentiment, resolution), and Behavior Analytics. Data is ingested via OpenTelemetry, CDP or queried directly from your data warehouse.
Kubit connects agent actions with user behavior. Standard observability tools tell you what broke, but Kubit tells you why the user was frustrated. Instead of bouncing between analytics dashboards and raw JSON to figure out why an agent failed, we feed that complete context straight into your coding agent via MCP. We give you the exact visibility and skills you need to debug, optimize, and solve it where you build it.
Unified Product Analytics
If you are building AI agents, you already know the context-switching nightmare. Standard observability tools tell you what broke (latency, token limits, system errors), but they can't tell you why the user was frustrated. To get the full picture, you have to manually stitch together clickstream events, prompt versions, and raw logs across many different tabs. It is slow, disjointed, and leaves you guessing.
Kubit fixes this. We map how users behave directly to how your agents reason, feeding that context straight into Claude Code via MCP. Instead of drowning in raw JSON traces, your coding agent instantly sees:
User intent and sentiment
Friction and delight signals
Exact tool call trajectories
User behavior before and after agent interactions
We give you the exact context you need to debug and ship reliable AI, right where you code.
How Kubit Works
Kubit is the only end-to-end analytics platform that natively correlates autonomous AI agent actions with human user behavior on a warehouse-native foundation. It feeds actionable insights into developer's workflow so you can debug, optimize, and self-fix all in the coding agent.
Conversation Intelligence: Go beyond simple LLM observability. Extract intent, sentiment, and resolution directly from your traces to understand the root cause of user frustration—insights that normally stay buried deep within raw JSON logs.
Total Visibility across Agent and User Behavior: Full context, all in one place. Correlate user action directly with your agent's reasoning chain. We stitch together clickstream events, LLM traces, tool calls, prompt versions, and user intent—so you can stop manually piecing the puzzle together across six different tools.
Headless Analytics, Solve It Where You Build It: Turn unpredictable AI into a measurable product. Kubit feeds exact user intent and friction signals straight into Claude Code via MCP, allowing your coding agents to trigger auto-fixes and optimize performance without ever opening a browser.
OTel, CDP or Bring Your Own Warehouse: Get started immediately through OpenTelemetry or CDP integration while maintaining full control over sampling, filtering and masking. Or Bring Your Own Warehouse with your BI schema with our self-service Data Modeler. Your data
Features Built for the AI Product Engineer
Product Analytics for LLM Observability: Go beyond simple performance monitoring and error tracking. With Kubit, you get advanced, self-service analytics to discover patterns at scale. Track token costs and error rates by intent and friction signals, explore escalation funnels, and follow user retention by cohort before and after agent interactions.
Support for Langfuse, LangSmith, Arize, and OTel: Integrate seamlessly with all major LLM observability frameworks or instrument your AI application with OpenTelemetry from scratch. Kubit does the heavy lifting for you and automatically applies industry best practices.
Conversation Intelligence Without Limits: Understand the why behind your metrics by analyzing the intent and sentiment of users interacting with your agent. Unlike expensive LLM-as-a-Judge or BYOM approaches, Kubit efficiently enriches 100% of your traces with zero sampling.
Coding Agent Integration and Self-Healing: The magic happens right where you code. Embedded directly into Claude Code and Cursor via MCP, Kubit equips your coding agents with the live context and skills needed to debug and auto-fix without ever opening a browser tab.
The Origin
Kubit began by offering warehouse-native product analytics to eliminate data silos. Since 2026, we expanded our focus to connect AI agent actions with human user behavior.
This shift came from our own experience. While building our AI Analyst feature, our engineers were tired of drowning in raw JSON just to figure out why agents failed and how it impacted user behavior. Observability tools only showed what broke (latency, token limits) but not why users were frustrated. Stitching together clickstream events, prompt traces, and system logs across different tools was a context-switching nightmare. So, we fixed it.
Our mission is to turn unpredictable AI into measurable, optimizable products. We map user intent directly to LLM reasoning chains and pipe that context straight into your coding agent via MCP. Leveraging our deep roots in product analytics and a uniquely flexible warehouse-native architecture, we are building the future of Product Analytics.
Our Philosophy
Context is King: Raw LLM traces are useless without user intent, sentiment or behavior insights.
Action Over Observation: True insight requires merging user behavior events with telemetry data into actionable insights, with the cloud data warehouse as a single source of truth.
Solve It Where You Build It: Dashboards are giving way to "headless" analytics, which should be directly consumed by your coding agent to get insights, find root causes and fix issues without much human intervention.
Next steps
Overview of Kubit
Explore features for Agent Observability, Conversation Intelligence, and Behavior Analytics
Set up the foundation: Warehouse-native