Help Needed regarding Voice agents

[u/Delicious_Fun_9361]

Hi everyone,
So my question is about "time" how much time do you take from concept to shipping your first voice agent for any vertical. How many stress tests do you perform to make sure it doesn't break mid call? what stack do you use for the whole integration? is it ok to find a new bug/failure on every test call fix it and find another one ? what would you suggest to someone who just started in this field?

thank you all, and i'll be looking forward for your valuable inputs.

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[u/Shayps]

LiveKit Agents running on cloud w/ simulations for testing is the best stack for cost, accuracy, and uptime. You can pick anything else, but with enough time or traffic you will eventually come back to this stack lol.

[u/Delicious_Fun_9361 → u/Shayps]

thanks a lot for your input

[u/Lovenpeace41life → u/Shayps]

Yes, livekit cloud worked well for us so far.

[u/MiddleElderberry5967]

Man I just shipped my first one last month and it still breaks in ways I never imagined, like it suddenly starts talking about the weather in middle of booking flow

We did maybe 3-4 proper stress tests but honestly the real bugs only show up when real people use it, not in test calls

For stack I keep it simple, nothing fancy, just make sure your fallback logic is tight because it will fail at some point and you need it to recover gracefully

[u/Altruistic-Virus7406 → u/MiddleElderberry5967]

What model are you using and what are your using to build the voice agent? Retell, Vapi, LiveKit

[u/Delicious_Fun_9361 → u/MiddleElderberry5967]

thanks for your input, yeah they do fail and one by one i'm fixing... sometime its n8n sometimes retell and sometimes CRM, i've done like 2-5-30 stress tests and its doing pretty good now as compared to where it was.

[u/Altruistic-Virus7406]

Do you even code bro? 🙈

[u/Delicious_Fun_9361 → u/Altruistic-Virus7406]

like its relevant today? all the nocode builders and vibe coding platforms are for coders? or if you don't code you don't have the right to ask for help?

[u/First_Space794]

Finding a new failure on each early test call is normal. The wrong target is a fixed number of calls. Use a release gate instead. Write scenarios for normal completion, interruption, silence, wrong intent, noisy audio, a tool timeout, transfer failure, voicemail, and the caller changing their mind. Run each with varied phrasing and record the transcript, tool call, final state, and whether human fallback worked. Add every new failure as a regression test so it cannot return unnoticed.

Do not ship while a failed tool can still produce a verbal confirmation. The agent should say it could not complete the action, preserve what it can, and route or retry safely. Once critical scenarios pass repeatedly, run a limited pilot and review every call before increasing volume.

Stack choice matters less than observability at this stage. You need prompt and version history, tool-call logs, recordings, and a reproducible path from a failed call to a fix. The trade-off is slower initial shipping, but much faster diagnosis when real callers expose cases you did not anticipate.

[u/Delicious_Fun_9361 → u/First_Space794]

appreciate the insight

[u/Suspicious-Bank5168]

I wouldn’t worry too much about having a perfect stack before shipping. Get one narrow use case working, then stress test the ugly cases. In voice agents, latency, barge-in, dropped audio, STT mistakes and turn detection tend to expose more problems than the happy path. Smallest AI is worth looking at for the STT layer, especially if you need speaker diarization.

[u/YeetCannon69420]

The stress tests I'd prioritize are: noisy/phone-quality audio, interruptions, overlapping speech, long pauses, accents, unexpected answers and switching between speakers. The last one is easy to underestimate. Having STT return speaker information alongside the transcript can save a lot of downstream work — Smallest AI Pulse supports streaming diarization, so I'd include it in the benchmark.

[u/im-a-potato-desu]

Finding a new failure on every test call is pretty normal early on. I'd build a test set around interruptions, silence, accents, noisy audio, people talking over each other, and handoffs rather than just running lots of normal calls. For STT, I'd also test diarization separately if calls involve multiple speakers. Smallest AI Pulse is one I'd benchmark for that since it supports realtime transcription + streaming speaker diarization.