Observability for voice AI agents: what's actually working for you?

cdutr
Mon, 09 Feb 2026 20:23:31 +0000
Agents

I’ve been running LiveKit voice agents in production across a few different projects, and each one ended up with a different observability stack. One uses Langfuse for LLM tracing, another Sentry for error tracking and performance, another Datadog module for LLM observability and APM, and the most recent one Arize AI for tracing, evaluation and experiments.

None of them feel purpose-built for voice AI. Langfuse is great for tracing LLM calls and prompt versioning, but it has no concept of audio latency or turn-taking. Sentry catches errors well and the LoggingIntegration works with the Python agents SDK, but it doesn’t give you voice-specific metrics out of the box. Datadog has the broadest coverage but the LLM-specific features feel bolted on (and it can get expensive). Arize is strong on evaluation but the real-time monitoring side is still maturing

What I really want is something that understands the full voice pipeline: STT latency, LLM TTFB, TTS generation time, end-to-end turn latency, interruption rates, and tool call success rates, all correlated per session. Right now I’m stitching that together manually with AgentMetrics events and custom dashboards

Would love to hear opinionated takes. What are you using for observability? Are you happy with it? Has anyone built a good custom dashboard on top of AgentMetrics?

9 posts - 5 participants