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7/1/2026
The 2am call that dropped before the user finished talking, and the week I spent finding out why my tracer never saw it

The 2am call that dropped before the user finished talking, and the week I spent finding out why my tracer never saw it

Short summary

A dropped call at 2am revealed that standard LLM tracing misses audio-layer failures in voice agents. After evaluating six observability tools, the author found OpenTelemetry-native options can capture audio events if you instrument them, while LLM-focused tools excel at their narrow scope. Recommendation: choose an OTel tool and invest in emitting audio spans where voice-agent failures actually live.

  • •Voice agent failures live in the audio layer (ASR, endpointing, TTS, barge-in detection), not the LLM call—standard LLM tracing is blind to them
  • •OpenTelemetry-native tools (Langfuse, Phoenix, Laminar, traceAI) can represent audio spans if you build them; LLM-focused tools (Helicone, LangSmith) excel at their scope
  • •Actionable insight: pick an OTel tool and spend your effort emitting custom audio spans, not comparing dashboards

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
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