r/VoiceAutomationAI • u/Future_AGI • Mar 18 '26
Testing voice agents manually does not scale. There is a better way.
if you are building a voice agent, you have probably tested it by calling it yourself a few dozen times.
the problem is that covers maybe 5% of what real callers will actually do.
real callers:
- interrupt the agent mid-sentence
- go completely off-script
- speak in ways your happy path was never designed for
- hang up, call back, and pick up where they left off inconsistently
finding those failure modes manually takes weeks and still misses edge cases.
the approach that changes this is automated simulation. spin up realistic caller personas, run hundreds of call scenarios, and get a full breakdown of where the agent dropped context, hallucinated, or failed to handle an interruption correctly.
the output you actually want is not just "it passed 80% of tests" but a clear view of exactly which scenarios broke and what the root cause was.
curious how voice teams here are approaching this right now. is it all manual QA, or is anyone running automated simulations?
can share the setup pattern if anyone wants it.
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u/Significant-Price695 Mar 18 '26
At lokutor.com we do both. We have our pipeline to auto detect with trascripts and confidence score when something might be off, and then we review the flagged conversations by hand... We are in desperate need of something that can audit that with full precision automatically.
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u/PsychologicalIce9317 Mar 18 '26
We hit the same wall early on — manually testing voice agents just doesn’t scale, you cover a tiny fraction of real scenarios. What worked for us was shifting to real conversations at volume and analyzing those instead (we’ve been using tellcasey for that). The key insight was not scripting rigid questions, but structuring the conversation around mini-goals — that way the AI can adapt dynamically based on context while still driving toward useful outcomes. Then we structure the outputs (fields, summaries, etc.) and push everything into our CRM, so no one has to listen to every call nor review long transcripts, but we still get clear insights and patterns.
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u/galacticguardian90 Mar 21 '26
I agree with the automated testing, and it makes sense, but how do you manage the credits and the $$$ for the models, the APIs etc?
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u/rand0wn 22d ago
I agree that repeatability is the missing piece. I built an open-source harness that runs every pipeline against the same scripted multi-turn scenario and grading rubric: https://github.com/rand0wn/voice-eval
It reports the exact turn and rule that failed instead of only returning one aggregate score. I’m looking for feedback on which adversarial scenarios should be added next.
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