LLM observability tools are blind to the voice layer. Here is what I checked 6 of them for.

Dev.to / 6/19/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage

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Key Points

The article argues that most LLM observability tools miss the core failure modes of voice agents because they only trace the LLM call (prompt/completion/latency) rather than the audio layer.
It highlights key audio-layer metrics that determine whether a voice agent feels responsive—such as end-of-turn detection, ASR latency/confidence, barge-in handling, and time-to-first-audio.
After checking six tools (Langfuse, Helicone, Arize Phoenix, LangSmith, Braintrust, and Laminar), the author finds that only OpenTelemetry-native approaches make it straightforward to add custom spans for audio events alongside model spans.
The author concludes that selecting an observability tool for voice agents should focus less on advertised LLM-tracing features and more on whether the platform lets you instrument audio-layer stages so performance issues can be traced to specific stages rather than guessed.

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