The Transcript Looked Fine. The Call Wasn't. — Debugging Voice Agents, Arize
Source
youtube.com
Author
AI Engineer
Date
Why it matters
Transcripts hide dead air, interruptions and wrong-order refunds. The talk shows tracing audio with OpenInference conventions and running evals on the audio itself, which voice agent teams need to catch real failures.
Key takeaways · AI-distilled
In Fuad Ali's example, the transcript shows an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → correcting itself, while the audio shows 2.4 seconds of dead air, an interruption, and a refund issued on the wrong order.
Arize's recommended view puts audio, transcript and trace in one session, because latency, turn-taking and transcription errors are invisible in a text log.
Audio-native evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → score sentiment, latency, interruptions and task success on the recording itself, and OpenInference conventions give real-time audio one trace schema across providers.
Ali closes with agent experiments that test fixes against failed traces, pointing toward self-healing voice agents built from monitors, investigation agents and replayed fixes.
Terms in this piece · Glossary
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
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