video1.0<iframe src="https://www.loom.com/embed/233b6074ec874490b124afb2227c6dda" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/233b6074ec874490b124afb2227c6dda-f7cfc7abf2ddf2c9.gif670.054This Loom demonstrates Cabra, a platform for benchmarking and stress testing voice agents under realistic conditions before production deployment. It generates natural call dialogues, adds filler words, converts the text to audio, applies a telephonic codec, adds background noise, and streams the degraded audio to the agent to test speech-to-text and escalation behavior. In a demo scenario with a frustrated caller complaining about a deducted amount, the agent scored 23% overall and produced garbled ASR output due to language mixing and noise. The trace identified key failures: it did not collect a required account number despite the user prompt, and it escalated to a human even when escalation was instructed not to occur.