<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/bda93fa32ade416d9c45f85c61c0362a&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/bda93fa32ade416d9c45f85c61c0362a-da152a83a0499115.gif</thumbnail_url><duration>375.808</duration><title>Observability Dashboard for AI Agent Calls</title><description>This Loom explains the Observability Dashboard in Regal’s Conversation Intelligence, showing real time call metrics across AI agents. The dashboard can be filtered by date range, agent, and call type, and it displays call minutes plus end to end latency with P50 and P90 measures. It also tracks interruption rates from both the contact and the agent to identify confusing prompt points, with follow up viewing in transcripts and the agent’s prompt for more granular diagnosis. Finally, it covers action latency and failures, showing latency levels and related failure information.</description></oembed>