> ## 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. # Choose a metric > Find the right metric by what you want to measure. The [Metric Library](/concepts/metrics/overview#the-metric-library) is organized by *how* each metric works. This page flips that around: start from **what you want to measure** and jump straight to the metrics that measure it. **Just starting out?** A solid baseline for most agents is [Latency](/concepts/metrics/types/statistical#latency), [End Reason](/concepts/metrics/types/deterministic#end-reason), and one custom [Binary LLM Judge](/concepts/metrics/types/llm-judge#binary-llm-judge) for your specific success criteria. Add more as you learn where your agent struggles. ## Did the agent do its job? (task resolution & correctness) | Metric | What it tells you | | ------------------------------------------------------------------------------------ | ----------------------------------------------------------- | | [Binary LLM Judge](/concepts/metrics/types/llm-judge#binary-llm-judge) | A yes/no answer to your exact success question | | [Composite Evaluation](/concepts/metrics/types/llm-judge#composite-evaluation) | Several pass/fail criteria scored together | | [End Reason](/concepts/metrics/types/deterministic#end-reason) | How the conversation ended (resolved, transferred, dropped) | | [Match Expected Output](/concepts/metrics/types/deterministic#match-expected-output) | Whether the outcome matches a known-correct value | ## Was it fast and responsive? (latency & reliability) | Metric | What it tells you | | ------------------------------------------------------------------------------------------ | ----------------------------------------- | | [Latency](/concepts/metrics/types/statistical#latency) | How long the agent takes to respond | | [Time to First Audio](/concepts/metrics/types/statistical#time-to-first-audio) | Delay before the agent starts speaking | | [Agent Fails to Respond](/concepts/metrics/types/deterministic#agent-fails-to-respond) | Turns where the agent went silent | | [Agent Needs Reprompting](/concepts/metrics/types/deterministic#agent-needs-reprompting) | Times the user had to repeat themselves | | [Agent Repeats Itself](/concepts/metrics/types/statistical#agent-repeats-itself) | Looping or repeated agent responses | | [LLM / STT / TTS Time to First Byte](/concepts/metrics/types/trace#llm-time-to-first-byte) | Where latency comes from in your pipeline | ## Does it sound natural? (voice quality — voice agents) | Metric | What it tells you | | ------------------------------------------------------------------------------------------ | ---------------------------------------------- | | [Interruption Rate](/concepts/metrics/types/statistical#interruption-rate) | How often the agent talks over the user | | [Speech Tempo](/concepts/metrics/types/statistical#speech-tempo) | Whether the agent speaks too fast or too slow | | [Pitch Variability](/concepts/metrics/types/statistical#pitch-variability) | Monotone vs. natural intonation | | [Volume-Pitch Misalignment](/concepts/metrics/types/statistical#volume-pitch-misalignment) | Unnatural volume/pitch mismatches | | [Voice Quality](/concepts/metrics/types/statistical#voice-quality) | Overall acoustic quality of the agent's speech | See the [Statistical metrics](/concepts/metrics/types/statistical) page for the full set of acoustic and prosody checks (background noise, artifacts, vocal fry, and more). ## How did the customer feel? (sentiment & experience) | Metric | What it tells you | | ------------------------------------------------------------------------------ | -------------------------------------------------------- | | [Transcript Sentiment](/concepts/metrics/types/ml-model#transcript-sentiment) | Sentiment inferred from the text | | [Audio Sentiment](/concepts/metrics/types/ml-model#audio-sentiment) | Sentiment inferred from the audio (tone, not just words) | | [Composite Evaluation](/concepts/metrics/types/llm-judge#composite-evaluation) | A blended experience score across criteria you define | ## Did it follow the rules? (compliance & scripting) | Metric | What it tells you | | ----------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | | [Transcript Regex Match](/concepts/metrics/types/deterministic#transcript-regex-match) | Whether required phrases appear — or use absent-match to enforce "never say X" | | [Binary](/concepts/metrics/types/llm-judge#binary-llm-judge) / [Categorical LLM Judge](/concepts/metrics/types/llm-judge#categorical-llm-judge) | Policy, tone, and disclosure checks | If your agent follows a defined call flow, add **Workflow Verification** to catch off-path behavior — generate the workflow in the [Agent creation flow](/concepts/agents/workflow) and the metric re-traces it against the transcript. ## Did it do the right things behind the scenes? (tools & traces) | Metric | What it tells you | | ----------------------------------------------------------------------------------- | ---------------------------------------------------------------- | | [Tool Call Count](/concepts/metrics/types/trace#tool-call-count) | Whether the agent called the tools it should have | | [Custom Trace](/concepts/metrics/types/trace#custom-trace) | Any value extracted from your OpenTelemetry spans | | [API State](/concepts/metrics/types/deterministic#api-state) | State your backend reports for the call | | LLM Judge with [Trace context](/concepts/metrics/configuring-metrics#trace-context) | Verify tool usage and order that isn't visible in the transcript | ## Was the audio transcribed accurately? (STT accuracy) | Metric | What it tells you | | --------------------------------------------------------------------------- | ------------------------------------------------- | | [STT Word Error Rate](/concepts/metrics/types/trace#stt-word-error-rate) | How accurately speech was transcribed | | [Transcription Error](/concepts/metrics/types/ml-model#transcription-error) | Likely transcription mistakes in the conversation | ## What did it cost? (usage) | Metric | What it tells you | | ---------------------------------------------------------------- | -------------------------------- | | [LLM Token Usage](/concepts/metrics/types/trace#llm-token-usage) | Tokens consumed per conversation | *** Once you know which metrics you want, [add them to a run](/concepts/metrics/quickstart). For anything custom, see [Write judge prompts](/concepts/metrics/writing-judge-prompts) and [Configure metrics](/concepts/metrics/configuring-metrics).