Kubit

About Kubit

Kubit links AI agent execution traces with user behavior data to diagnose why features succeed or fail. It ingests traces from coding agents and correlates them with clickstream events, A/B tests, and business metrics inside the user’s own data warehouse. The platform enriches traces with intent and sentiment analysis, enabling teams to pinpoint where hallucinations or poor tool calls break conversion flows. It supports headless analytics for coding agents and embeds product analytics into agent loops via MCP for automated verification. Kubit emphasizes open standards such as OpenTelemetry and SQL, avoiding proprietary formats and vendor lock-in. Zero-copy architecture keeps PII and sensitive data within the warehouse, and sampling or filtering can be adjusted without reprocessing. The tool is designed for data analysts, product managers, and engineering teams building AI-powered products.

Key features

• ✓Enrich traces with intent and sentiment analysis
• ✓Measure AI outcomes using user engagement and retention
• ✓Track user-agent funnels and conversion drops
• ✓Map AI journeys including re-prompts and rage clicks
• ✓Build granular cohorts across agent and user data
• ✓Integrate via OpenTelemetry, CDP, or SQL
• ✓Sample, filter, and mask sensitive data
• ✓Embed analytics into agent loops via MCP

Use cases

• ✓Debugging AI agent failures by linking traces to user exits
• ✓Optimizing AI features based on behavioral insights and outcomes
• ✓Automating verification of agent actions using embedded analytics

Frequently asked questions about Kubit