> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usetuner.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Technical Evals vs. Business Evals

> Why technical metrics like WER and latency aren't enough, and how business evals measure whether your voice agent actually achieves its goal.

When evaluating a voice AI agent, it's easy to get lost in a sea of technical metrics like Word Error Rate (WER), latency, and transcription accuracy. These metrics are important—they form the technical foundation of a good conversational experience. However, at Tuner, we believe they are not enough.

Think about it this way: an agent can have a perfect WER and still fail to understand a user's intent. It can have flawless grammar and still violate a critical industry regulation. It can respond in under 500 milliseconds and still leave the customer without a booked appointment. This is why our philosophy at Tuner is built on a simple truth: **a technically perfect agent that doesn't achieve the business goal is a business failure.**

This is the critical distinction between **Technical Evals** and **Business Evals**:

* **Technical Evals** measure *how* the agent works. They focus on the performance of the underlying STT, LLM, and TTS models.
* **Business Evals** measure *what* the agent accomplishes. They focus on the outcomes of the conversation and whether the business goal was achieved.

To truly understand agent performance, you need both. But the focus must shift from a purely technical view to a business-first approach. This is the core of our direction at Tuner.

**Ready to build a business-first evaluation strategy?**

> **Next:** Learn how to implement the 4-Layer Framework we've built into the core of the Tuner platform.
> [**Go to The Business Evals Framework →**](/user-guide/best-practices/custom-evaluations/business-evals-framework)