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

# Overview

> Metrics are used to understand and evaluate the performance of your AI Voice Agent

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## Metric Definition

Metrics are essential tools for evaluating AI Voice Agents, acting as judges that assess call quality. They analyze call transcripts and sometimes recordings to provide scores based on specific criteria. This evaluation covers aspects like clarity, relevance, and instruction following. By assigning scores, metrics help identify strengths and areas for improvement. Regular analysis of these metrics enables organizations to make data-driven decisions, enhancing voice agent performance and user satisfaction. Ultimately, metrics guide the optimization of strategies, ensuring that voice agents effectively meet user needs and organizational goals.

## How to Use

#### Adding Metrics

From the metrics section on Cekura's dashboard a user can add/define metrics. Metrics can be broadly categorised as:

1. **Pre-defined Metrics**: These are generic metrics such as CSAT, pitch, latency, and interruption count. These are defined by Cekura. You can read more about each pre-defined metric [here](/documentation/key-concepts/metrics/pre-defined-metrics).

2. **Instruction Following Metric**: This metric is part of the pre-defined metrics and is used to automatically find and categorize issues in the call based on agent description. This saves you from having to define 100s of metrics. [Read More](/documentation/key-concepts/metrics/instruction-following-metric)

3. **Custom Metrics (User-defined)**: These are metrics defined by the user and can be of types such as Boolean, Rating, and Enum.

Note that you can create metrics at project and agent level. Those metrics that are project level can be enabled or disabled for the agents of that project.

#### Running Metrics

* **Testing**: Each evaluator has a list of metrics attached to it. When running simulations, a metric is executed when it is both attached to the evaluator and enabled on the agent.
* **Observability**: We pick relevant metric to run at the end of the call depending on how the call went