Kubit: agent analytics for products where AI and users interact

Kubit announced on August 10, 2026 a significant repositioning of its product analytics platform toward what the company calls agent analytics. The core problem it addresses is one that has emerged alongside the broader adoption of AI-powered products: traditional observability tools track system performance — latency, token usage, error rates — while product analytics tools track user behavior. These two data streams have historically lived in separate systems, making it difficult to understand why a user abandoned an AI feature, re-prompted, or failed to convert.

The updated Kubit platform connects agent traces — the sequence of tool calls, model responses, and reasoning steps that an AI agent executes — directly to user engagement metrics such as session depth, retention, and funnel drop-off. The platform also enriches those traces with user intent and sentiment signals. The result is a unified view where a product team can see not just that users dropped off after a particular agent interaction, but what the agent did at that step and what the user was trying to accomplish.

Technical implementation runs through OpenTelemetry for trace collection, with a warehouse-native architecture that stores all data in the team’s existing data warehouse. A headless MCP integration allows coding agents to pull analytics context directly, enabling AI-assisted debugging of agent behavior.

For product managers building AI-powered features, the gap this closes is real. Debugging a failing AI feature has typically required assembling context from multiple tools — a logging dashboard, a usage analytics platform, and a qualitative feedback channel — and manually correlating the signals. A system that surfaces the correlation automatically shortens the iteration cycle and changes what questions a PM can ask without involving a data engineer.

Product teams that have shipped AI features and noticed high re-prompting rates or unexplained drop-off in agent-driven flows are the most direct audience. The platform launched publicly on Product Hunt on August 11, 2026, where it placed in the top ten for the day.