Why Transcripts Aren’t Enough for Debugging Voice AI (And What to Use Instead)

Voice AI teams still rely on transcripts for debugging.
But a transcript only shows the surface of the system. The real debugging context lives deeper.

A voice call is a pipeline:
Audio → ASR → LLM → Tools → TTS → Audio Output

A delay in ASR affects the LLM.
A stalled tool call affects timing.
A weak TTS response breaks user experience.

Transcripts don’t show latency patterns, tool behavior, blocked branches, or reasoning failures.

This is why we built Voice Observability in SIMULATE.

Instead of logging text, we trace the entire execution:

• Audio in/out with timestamps
• ASR events and confidence shifts
• LLM reasoning paths and tool calls
• TTS generation + round-trip latency
• Behavior regressions across runs

You also get a single, continuous session view, no stitching logs from multiple systems.

And it works across stacks like Vapi, Retell, LiveKit, Pipecat, plus custom voice pipelines.

Voice agents are finally hitting production scale.
Relying on transcripts is like debugging a distributed system with print statements.

Full observability is the engineering baseline.

🔗 Learn More -> https://shorturl.at/Jfu6S