The typical journey for everyone starting with Voice AI looks the same: you see a demo online, create your agent, connect RAG, and realize the voice sounds amazing. The latency is good. It feels like you are very close to a production-ready system.
It’s a lie.
This “almost there” phase can easily stretch from one month to half a year. You lose time, money, and patience. Here are the five biggest mistakes I made when building Voice AI, and how you can avoid them.
1. Operating Without Observability
With LLMs being non-deterministic, you get randomized results even with temperature set to zero. Real users speak differently than you do and use the platform in unexpected ways.
Without observability, you are operating blindfolded. You don’t know what causes dropped calls, user frustration, or what actually makes users stay.
The Fix: Plumb in observability from day one. In my experience, implementing proper tracking saved clients six figures and significantly improved call quality and duration.
2. Ignoring Your Best Human Agents
A common oversight is not working closely with the people who actually pick up the calls for the business you are automating (e.g., in-house receptionists for a dental clinic).
Human agents do complex things. Even when following a script, they assume context and connect dots easily. LLMs do not have this capability. If you give an LLM a script that human agents have used for decades, 30% to 40% of it won’t work out of the box.
The Fix: Work closely with the people handling customer calls. Understand the unspoken assumptions they make and manually adjust your prompts and logic to account for them.
3. Skipping Automated Regression Testing
I wasted immense amounts of time not having a proper test suite because I assumed one small tweak wouldn’t break the whole platform. It did.
LLMs break in ways that don’t make sense to humans. Without regression testing to ensure your tool calls, happy paths, and core logic are functioning, your iteration speed drops to zero.
The Fix: Voice AI applications require the highest possible iteration speed, not the largest amount of features. Build automated regression tests to verify that every change improves the system rather than breaking it.
4. Not Dogfooding Your Own Application
If you just add RAG and more functionality, people will still drop off calls and hate talking to your AI. Often, the scope of the agent is simply too big, or edge cases are poorly handled.
If you aren’t going through the same struggles as your users every day, you won’t understand why the system is failing. In my case, a lack of dogfooding led to unhandled edge cases in specific routing paths that frustrated users.
The Fix: Make calls to your own agent every single day. Test the boundaries of what it can handle.
5. Making Assumptions Based on Hype
You will see posts claiming a specific LLM solved all latency and intelligence issues, or that a new TTS model is flawless. While the raw technology might be great, you cannot assume it will work perfectly for your specific use case.
For example, upgrading to a better TTS provider improved the voice quality, which made users assume the system’s intelligence was equally high. When the agent failed to understand complex queries, users were even more frustrated. We paid more for the TTS, but nobody won.
The Fix: Make no assumptions. Test everything. Voice AI pipelines require constant iteration.
For more insights on building reliable Voice AI, check out my breakdown of HIPAA and GDPR compliance for Voice AI, or read my conversation with Ophir Samson on the three stages of Voice AI disillusionment.
Need Help Fixing Your Voice AI Pipeline?
If you are stuck in the “almost there” phase of your Voice AI deployment, you don’t have to figure it out alone. Let’s talk about how to implement proper observability, testing, and reliability for your production agents.