Analytics and insights for AI agent products

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

Teams building AI agents struggle to analyze and understand agent behavior across their product organization. Current solutions like LangSmith excel at technical tracing but lack collaborative analytics and self-serve insights, forcing non-engineers to request engineers spend hours manually investigating logs to debug or understand usage patterns.

Solution concept

A collaborative analytics platform for AI agent products that provides self-serve insights accessible to product, design, and engineering teams without SQL expertise. It surfaces usage trends, identifies patterns across agent conversations, enables quick root-cause analysis of issues, and aggregates intent data into actionable insights that scale with high conversation volumes and team size.

Who has it

Product managers, designers, and engineers building AI agent products, particularly those with millions of conversations monthly and thousands of unique intents across collaborative teams.

How they handle it today

Teams either build homegrown internal analytics solutions (expensive to maintain as request volume and agent usage scale), use LangSmith primarily for technical tracing (accessible only to engineers), or have non-technical team members request engineers manually investigate logs in LangSmith to debug issues or understand usage trends, a process that takes hours per inquiry.

Why now

AI agent adoption is scaling with millions of conversations per month, making homegrown solutions expensive to maintain. Large product teams need collaborative insights beyond what technical tracing tools provide, and the volume of intent data now requires automated data pipelines to be useful.

How it could charge

Subscription pricing based on conversation volume processed monthly, with team seats or roles, similar to analytics platforms like Google Analytics or Mixpanel.