r/AIVoice_Agents

I’m trying to figure out where people are drawing the line between an AI agent continuing the conversation and handing it to a person.

We’ve been looking at AI call center software like Bland or Vapi and some of the workflows get complicated pretty quickly.

The caller changes topics, corrects something they said earlier, an API lookup fails, they ask a followup about another issue then eventually need a human for one specific part.

At that point I don’t think 'did the AI contain the call?' is even the most interesting question.

I’d rather know whether it handled the conversation correctly up until the point where a transfer really made sense.

How are you measuring that?

Honestly I’m about ready to just quit voice AI entirely. I know everyone says their tech is good, but ours actually doesn't sound robotic and it completely automates the post call work. But none of that even matters because selling this stuff is a nightmare right now.

Every business owner I talk to has heard one terrible AI agent and instantly assumes they all sound like garbage. Or if you do get them interested, they have this weird expectation that they can pay 30 bucks a month and get a flawless fully integrated agent. The price anchoring from cheap wrapper products is making it impossible to charge what it's actually worth.

It's burning me out. It feels like every niche I try to target is just completely trash right now. We've tried running Meta and LinkedIn ads, but it seems like most service based businesses just aren't on those platforms enough, or they're just impossible to get a hold of through paid social.

What is everyone else doing to stay in the race right now? Has anyone figured out a way to actually scale this or get past the terrible market reputation? I'm hitting a wall here and could use some advice from anyone actually making this work.

Hey, I have been using n8n for a couple months now and have built a handful of workflows, currently I am attempting to build an outbound voice agent, so when clients fill out a form on my website they receive a personalized outbound call using the clients first name and key details gathered from the form. I got it to call me but it hangs up instantly and I am now feeling stuck. I am looking for ways to build a personalized voice outbound agent or I am looking for a way to fix my workflow. I DONT KNOW HOW TO PUT MY JSON ON HERE OR ELSE I WOULD IS HOW DESPERATE I AM

I am strongly considering using an ai receptionist for a number of reasons - I can’t handle the calls myself, training receptionists in the past have been tremendously difficult due to the technical nature of my business. We miss calls all the time. Humans are super slow, etc

How has your experience been? Are you booking more (because it answers all calls), are a lot of people hanging up?

Out of the box AIR are bad but some allow you to fine tune your AIR really well and it gets close to human speaking, reasoning and behavior.

Hi everyone,

I want to build a personal voice-activated AI assistant/agent that can execute tasks across both my PC (Windows) and mobile device based on voice commands.

Here is what I am aiming for:

Cross-Platform Control: Capable of automating tasks on PC (managing files, running scripts, app controls, browser automation) and basic mobile actions.

Voice-to-Action: Fast Speech-to-Text (STT) → LLM reasoning / Tool Calling → Execution → Text-to-Speech (TTS).

Architecture/Backend: Flexible to run via API (OpenAI/Gemini/Claude) or locally (Ollama/vLLM) with proper function calling / agentic frameworks.

Questions for the community:

1. What frameworks or agent architectures do you recommend for bridging OS-level actions (e.g., LangChain, AutoGen, CrewAI, or direct Python OS/pyautogui scripting)?
2. How should I handle the mobile ↔ PC sync and remote execution securely (e.g., local Flask/FastAPI server, WebSockets, or Home Assistant integration)?
3. Are there existing open-source projects or GitHub repos doing this well that I can study or contribute to?

Thanks in advance for the guidance!

How can I build shared context between WhatsApp and an AI voice calling agent?

I'm building an AI system where a customer can communicate with the same AI through WhatsApp and voice calls.

For example:

1. A customer starts chatting with the AI on WhatsApp.

2. During the conversation, they ask for a phone call.

3. The AI voice agent calls them.

4. The voice agent should already know the relevant WhatsApp conversation and continue from the same context instead of starting from scratch.

5. After the call, the customer returns to WhatsApp.

6. The WhatsApp AI should know what was discussed during the call and continue from that point.

And the reverse should also work:

Voice call → WhatsApp → same context

I want the customer to feel like they're talking to one AI, regardless of the channel.

I'm considering using a central customer ID linked to the phone number and storing the conversation history/customer information in a database, so both the WhatsApp agent and voice agent can access the same context.

However, I'm unsure about the best architecture.

- What is the best way to maintain shared context between WhatsApp and a voice AI agent?

- Should I use a central database/memory layer?

- How should I identify the same customer across both channels?

- How should the WhatsApp → voice context handoff work?

- How should the voice → WhatsApp context handoff work?

- How can I prevent the AI from getting confused by multiple summaries or different conversation contexts?

- Has anyone built something similar using WhatsApp Business API, n8n, GHL, or another CRM?

I'm looking for a practical, production-ready approach rather than just passing the entire previous transcript to the AI every time.

Most contractors I know lose 2–5 high-ticket jobs every week simply because they're under a sink, on a roof, or in an attic and can't pick up the phone. But traditional IVR menus suck, and most voice bots sound robotic with painful 3-second delays.

I built a low-latency inbound voice agent specifically designed for home services.

**What it's built to do on a live call:**

* Pick up in under 2 rings (24/7 coverage). * Ask job-specific triage questions (emergency vs routine, service address, issue description, scope). * Check live calendar availability and schedule the estimate/job directly. * Send an instant text/email to the contractor with the caller’s summary, full transcript, and audio link.

**Here’s the deal:**

1. Dial the sandbox number below and act like a real homeowner with an urgent or weird service request.
2. Try interrupting it, talk over it, change your address, or test how it handles scheduling.

**Call the sandbox line here:** `+1(240) 680 9235`

**What I’m asking:**

* If you test it, leave a brutally honest review in the comments. Tear apart the latency, tone, or logic—tell me what would make you hang up as a customer. * If you run a home service business and actually like how it handles calls, I will set up and deploy a custom pilot version for your business for free in exchange for your raw feedback during the trial.

Looking forward to the roasts—let me know what breaks.

Most AI voice SaaS landing pages sell the voice.

“Sounds human.” “Natural conversations.” “AI receptionist.” “AI sales agent.” “AI support agent.”

But if you strip the demo polish away, the boring failure is usually earlier.

The agent hears the user wrong.

Then everything after that gets worse:

wrong transcript → wrong intent → wrong tool call → wrong CRM update → wrong summary → wrong follow-up → angry customer

A voice can sound slightly robotic and still be useful.

But if it hears “don’t cancel” as “cancel,” the product is dead.

For voice SaaS, I’d build the stack around the listening layer first:

call/audio input → Smallest AI Pulse for real-time STT → entity checker → workflow engine → Stripe / Calendly / CRM action → confirmation message → audit log

The STT metric I’d care about is not just WER.

It’s:

• did the right task happen?

• did the right field get filled?

• did the user correction get captured?

• did the summary match the call?

• did the system avoid acting when uncertain?

For AI voice SaaS, “heard correctly” is a retention feature.

Founders building voice products: are you measuring transcript accuracy or task accuracy?

Three person team here. We get calls throughout the day but cant justify hiring someone to sit on phones. Looked into voice AI but most options seem built for bigger companies with IT teams and budgets to match. Anyone running something lean actually find a setup that works without a ton of hand-holding?

We are a startup and have built our AI Voice Agent stack. It runs decently and after painstaking efforts with our mule partner we were able to narrow down the architecture and design to curb failure points as much as we could. However, the biggest pain point we have stumbled across is the telephony carrier!

There is no reliable one! Here is what we have tried and encountered so far:

1. Vobiz: Our current provider. Easy enough to authenticate using personal Adhaar and PAN. They have one of the easiest integrations, setup, and starting curve. the plan pricing is optimum to get started and they don't setup minimum deposit walls. Their API is fantastic to the point that it supports almost all the features that you would need. That being said, there have been issues that we have been facing: The call quality and call handling has been giving us some issues intermittently, where the end result is silent calls. There are other issues including mid call disconnect/call-silence, which I hope to resolve with Vobiz support. Will update if we are or aren't able to resolve this with Vobiz.
2. VoiceLink: Again easy starting with adhaar and pan verification. Decent setup and API support but has a steep starting paywall. They need a minimum of 5000 Rs to get you started without even allowing you to test if their service is compatible and a good fit for your stack. API is good but not great. For example, during our tests Call Transfers would fail there was no way to trace/figure out what happened?
3. Exotel: Hardest to start so far. Requires proper company documentation. Good free tier. Inconsistent pricing information. Hidden credit consumption, cost, and plan information. High paywall with a minimum of 10000 Rs to get started. Support has been great so far where you are actually able to connect with a human who can answer your questions in contrast to the above 2. Credits are timebound for 7 days. After that the free tier ends. API has been good so far, but we are still evaluation Exotel.
4. Plivo: The most scummy of them so far. We couldn't even get an account. They force/tried to sell us their $1000 USD per month plan to get started.
5. Twilio: The most easiest to get started with it ticking green in all the check boxes. Unfortunately, they are not functional in India.

Anyone has any other carrier that they have been working with and can recommend?

I always love to see useful open source projects. The title says it all, thanks the Devs for putting this out for free as open sourced

How do u fix hight latency after i talk to the agent it answers me after 3.5s i use deepgram for STT and TTS, gemini for llm and twilio i coded everty thing with python and fast api for the backend

Can u tell m where the problems could be and how to fix it

three ugly ones i keep thinking about:

•caller starts as “i’m calling for my wife” then halfway through talks like they’re the account owner

•kid screaming + tv playing + another person answering questions in the background

•booking API times out right after the agent starts saying “perfect, you’re confirmed…”

•i’m collecting these while building out a regression scenario library around TestMu Agent Testing.

•personas, accents, noise etc are useful, but only if there’s an actual assertion behind the chaos.

not just:

did the agent sound natural?

more like:

was the correct appointment actually booked?

did the transfer really connect?

did it avoid taking an action without verifying identity?

otherwise we’re basically benchmarking how gracefully the bot survives a bad conversation while ignoring whether it did the job correctly.

TestMu helps structure/replay the scenarios, but obviously it can’t invent every cursed thing real callers will do.

so stealing ideas from humans instead.

give me one ugly call condition your voice agent needs to survive.

i’ll turn the best ones from this thread into a public regression checklist.

I maked ai receptionist using vapi but to answer question it take some time ..some second how to avoid this ?

Sometimes echo does not know what I mean Or it thinks I'm talking to somebody else and does not respond even though I'm not

Not using it much , but I used the voice agent from the chat gpt app in my car today. I wants to know where i can fuel up my car the smartest way, cause I am in Netherlands , drive through Belgium to Germany this day. First he told me that perhaps in Belgium is cheapest , what is right so far. But he added, that I should use a gas station near German border and if it is between 18:00 and 22:00 I should drive to Germany cause in this time , the fuel there is on daily lowest price. That’s a total useless information, cause it is higher than Belgium on daily’s lowest price too, but the law in Germany has changed a few months before , that this rule is not right anymore. Why he told me this ? It expands the cost of my request for open ai and make the answer less good. After I told him this , he saw his fault and told me I should use a gas station beside the autobahn , 2-3 km/h away .. this is not a distance , it is a velocity. That’s not a simple fault it is fundamental , at this level of task he was ask to do for me. The answer that I want was : Belgium .. that’s it … there is so much shit going on ai , I don‘t believe this is the life changing technology Sam is selling .. it is a dead end in the way it actually goes