Every user who touches your AI feature looks equally engaged. Almost none of that tells you whether the session actually worked. AI fails quietly: it hallucinates, ignores instructions, and returns confident wrong answers, and standard product analytics can't explain any of it.

We ran into this while building our own AI agents, with no way to tell whether eval scores translated into real adoption. So we built Agent Analytics. It puts AI quality and product outcomes on the same identity graph, so traces, evals, and agent interaction behaviors sit next to conversion, retention, and revenue. You can finally answer the question every AI team is stuck on: is the AI actually moving the business?

Join us July 15 for a live demo. You'll see how to:

• Inspect any agent run end to end: prompts, tool calls, retrieved context to identify how AI fails silently.
• Score every production session with code-based or LLM-as-a-judge evals, alongside the eval stack you already use
• Connect agent quality to conversion to surface the failure modes that actually cost you users.
• Compare models on cost, latency, and downstream outcomes (could the cheaper model retain users just as well?).