> ## Documentation Index
> Fetch the complete documentation index at: https://docs.coval.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Metrics
> Turn a conversation into a measurable signal — what metrics are, and where to go next.
A **metric** turns a conversation into a measurable signal — a score, a yes/no, a category, a latency number — so you can tell whether your agent did its job and track how it performs over time. Coval includes built-in metrics, and you can author your own. Every metric works on both simulated conversations and live-monitored production calls.
Pick the path that matches what you're here to do:
New here? Create a metric and attach it to a run in a few steps.
Not sure what to measure? Find the right metric by goal.
Browse all metric types, organized by how each one evaluates.
Author LLM-judge metrics that score reliably.
## What is a metric?
Each metric assesses your agent in a different way. **Audio** metrics use recordings — simulated or live — to detect interruptions, measure speech tempo, assess latency, and more. **LLM Judge** metrics answer specific questions about your transcripts, so you can check your exact success criteria. Others include **sentiment analysis**, **regex matching**, **trace-based** checks on tool calls, and many more.
Some metrics are built-in and ready to use; others are **configurable**, meaning you supply a prompt, a pattern, or a threshold. Either way, you attach metrics to a run and Coval scores every simulation against them.
## The metric library
Every metric belongs to one of five groups, organized by **how** it evaluates a conversation. Browse a group to see the metrics it contains and how to configure each one.
| Group | How it evaluates | Metrics |
| ------------------------------------------------------ | ------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Deterministic](/concepts/metrics/types/deterministic) | Rule-based pattern matching, field lookups, and configured comparisons — no model inference | Agent Fails to Respond · Agent Needs Reprompting · API State · End Reason · Match Expected Output · Metadata Field · Music Detection · Transcript Regex Match · Words per Message (Threshold) |
| [Statistical](/concepts/metrics/types/statistical) | Deterministic timing, signal, and acoustic analysis of the call | Audio Duration · Interruption Rate · Latency · Speaking Time % · Time to First Audio · Words per Message · Abrupt Pitch Changes · Audio Frequency · Background Noise · Clipping / Codec / Dropout Artifact · Loop Detection · Non-Expressive Pauses · Pause Analysis · Phoneme Stretch · Pitch Variability · Spectrogram Pitch · Speech Artifact Anomaly · Speech Tempo · Syllable Rate · Vocal Fry · Voice Quality · Volume Variance · Volume-Pitch Misalignment · Agent Repeats Itself |
| [ML Model](/concepts/metrics/types/ml-model) | Purpose-built machine-learning models | Audio Sentiment · Timbre Drift · Transcript Sentiment · Transcription Error |
| [LLM Judge](/concepts/metrics/types/llm-judge) | A language model evaluates against your prompt | Binary · Categorical · Numerical · Audio Binary · Audio Categorical · Audio Numerical · Composite Evaluation |
| [Trace](/concepts/metrics/types/trace) | Computed from your agent's OpenTelemetry spans | Custom Trace · LLM / STT / TTS Time to First Byte · LLM Token Usage · Tool Call Count · STT Word Error Rate (+ Audio Upload) |
This table is organized by mechanism. If you'd rather find a metric by **what you want to measure** — task resolution, latency, sentiment, compliance, voice quality, tool use — start with [Choose a metric](/concepts/metrics/choose-a-metric).
## Build your own
Beyond the built-ins, you can author your own metrics — LLM judge prompts, regex checks, tool-call rules, metadata fields, and custom trace extractions.
Prompt structure, few-shot examples, and the techniques that make LLM-judge scores consistent.
Template variables, transcript scope, trace context, and thresholds.
For conditional evaluation logic — running a follow-up metric only when a trigger metric fires — see [Metric Chaining](/concepts/metrics/metric-chaining).
## Version history
Every config-changing save of a metric is recorded in its version history, so you can see how a metric's scoring configuration changed over time and tell which version a run scored against. See [Versioning](/concepts/versioning/overview) for how copy-on-save works and how to pull the history through the [v1 API](/api-reference/v1/metrics/metrics/list-metric-versions).
If you need a metric Coval doesn't have, contact us and we can build it for you.