configure-metrics

name	configure-metrics
description	Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
argument-hint	[agent-name-or-use-case]

Configure Metrics

Guide the user through selecting, creating, and attaching evaluation metrics for their AI agent using the coval CLI. Follow the phases below in order.

If $ARGUMENTS contains an agent name or use case, use it to skip the relevant question in Phase 1.

Phase 0: Preflight + Inventory

Step 1: Check authentication

coval whoami

If not authenticated, guide the user:

coval login

This prompts for an API key. Get one at https://app.coval.dev/settings (Organization > Manage > API Keys).

If the user doesn't have a Coval account, direct them to https://coval.dev to sign up.

Step 2: Inventory existing resources

Run these in parallel:

coval metrics list --format json
coval metrics list --include-builtin --format json
coval agents list --format json

Categorize the metrics inventory:

• Built-in: Metrics with created_by: "Coval" in the --include-builtin response. These are platform-provided and exist in every org (e.g., Latency, Turn Count, Audio Duration, Transcript Sentiment Analysis, etc.)
• Custom: User-created metrics (llm-binary, audio-binary, pause types)

Note the IDs of relevant built-in metrics — you'll need them for Phase 5.

Phase 1: Agent + Use Case Context

Ask:

1. "Which agent are these metrics for?"

   • Present existing agents as a numbered list from the inventory
   • If $ARGUMENTS matches an agent name, select it automatically

2. "What does your agent do?" (if not obvious from agent name or prompt)

   • customer_support — Customer Support
   • scheduling_booking — Scheduling & Booking
   • sales — Sales
   • insurance_claims — Insurance Claims
   • healthcare_intake — Healthcare Intake
   • restaurant_orders — Restaurant Orders
   • debt_collection — Debt Collection
   • it_helpdesk — IT Helpdesk
   • other — Other (describe it)

Capture the agent's type (voice, outbound-voice, chat, etc.) from the agent record — this determines whether audio metrics apply.

Phase 2: Metric Recommendations

Load references/metric-recommendations.md and build the recommendation list.

Built-in metrics (discover dynamically from coval metrics list --include-builtin --format json, look for created_by: "Coval"):

• Select relevant built-ins based on agent type:
  • All agents: Latency, Turn Count
  • Voice agents: Audio Duration, Transcript Sentiment Analysis, Audio Sentiment, Speech Tempo, Time To First Audio, Interruption Rate, Background Noise
  • Chat agents: Words Per Message, Transcript Sentiment Analysis

Use-case specific:

• One custom llm-binary metric per vertical (from recommendations file)

Voice agents only (type = voice or outbound-voice):

• Professional Tone (audio-binary) — custom, needs creation
• Pause Detection (pause, min 3.0s) — custom, needs creation

Present the recommendations:

Based on your <use case> agent, I recommend these metrics:

  [built-in]  Latency                 — Response time measurement
  [built-in]  Turn Count              — Number of conversation turns
  [built-in]  <other relevant built-ins based on agent type>
  [custom]    <Use Case Metric>       — <description from recommendations>
  [audio]     Professional Tone       — Voice quality (voice agents only)
  [audio]     Pause Detection         — Flags pauses > 3s (voice agents only)

Tip: List all available built-ins with coval metrics list --include-builtin --format json and identify them by created_by: "Coval". Recommend the ones most relevant to the user's agent type and use case.

Ask: "Accept these metrics? (yes / add more / remove some)"

• yes → proceed to Phase 3
• add more → ask what additional criteria they want to measure, add to list
• remove some → present numbered list, let them deselect

Phase 3: Custom Metric Creation

For each custom metric in the accepted list, guide through creation:

1. Name and description — pre-filled from recommendations, confirm with user
2. Type selection — load references/metric-types.md if the user wants to understand options
3. Configuration:
   • For llm-binary: Use the prompt template from recommendations. Ask if they want to customize it.
   • For audio-binary: Use the prompt from recommendations. Customize if needed.
   • For pause: Confirm min duration threshold (default 3.0s).

Create each metric:

# LLM Binary metric
coval metrics create \
  --name "<name>" \
  --description "<description>" \
  --type llm-binary \
  --prompt "<evaluation prompt>" \
  --format json

# Audio Binary metric (voice only)
coval metrics create \
  --name "<name>" \
  --description "<description>" \
  --type audio-binary \
  --prompt "<prompt>" \
  --format json

# Pause metric (voice only)
coval metrics create \
  --name "<name>" \
  --description "<description>" \
  --type pause \
  --min-pause-duration 3.0 \
  --format json

Capture the metric_id from each JSON response.

Phase 4: Critical Requirement Metric

Ask: "What's the #1 thing your agent MUST get right?"

If the user provides a requirement:

1. Create an additional llm-binary metric using the critical requirement template from references/metric-recommendations.md
2. Convert the user's requirement into a short Title Case metric name — do NOT use the raw requirement text as the name. Follow the built-in metric naming convention: short noun phrases like "Caller Identity Verification", "Issue Resolution", "Order Accuracy". Examples:
   • "The agent must verify caller identity before sharing account details" → "Caller Identity Verification"
   • "The agent should never promise features that don't exist" → "Feature Claim Accuracy"
   • "Make sure the agent collects the policy number" → "Policy Number Collection"
3. Use the user's full requirement text in the --prompt and --description fields — that's where the detail belongs.

coval metrics create \
  --name "<short Title Case name>" \
  --description "<user's full requirement text>" \
  --type llm-binary \
  --prompt "Given the transcript, did the agent satisfy this requirement: <user's requirement>? Return YES if the requirement was met. Return NO if the requirement was violated or not addressed." \
  --format json

Capture the metric_id.

If the user says "none" or "skip", proceed without creating this metric.

Phase 5: Attach to Agent

Collect all metric IDs:

• Built-in metric IDs from Phase 0 inventory
• Newly created custom metric IDs from Phases 3 and 4

Offer to attach as agent defaults:

I'll attach these metrics as defaults for <agent name>:

  <metric name 1>  (<metric_id>)
  <metric name 2>  (<metric_id>)
  ...

These will automatically apply to every evaluation run for this agent.

Ask: "Attach these as defaults? (yes / no)"

If yes:

coval agents update <agent_id> --metric-ids <comma_separated_ids>

Phase 6: Summary + Next Steps

Present all configured metrics:

Metrics configured for <agent name>:

  Type         Name                      ID
  ──────────   ────────────────────────  ──────────────────────
  built-in     Latency                   <id>
  built-in     Turn Count                <id>
  built-in     <other selected built-ins> <id>
  custom       <Use Case Metric>         <id>
  custom       <Critical Requirement>    <id>
  audio        Professional Tone         <id>
  audio        Pause Detection           <id>

  Attached to agent: <agent name> (<agent_id>)

Suggest next steps:

• Build test cases: "Use /build-test-suite to create test scenarios"
• Design persona: "Use /design-persona to create a simulated caller"
• Launch evaluation: "Use /quick-eval to run your first evaluation"
• If new metrics were created: "Use /build-dashboard to add your new metrics to a dashboard so you can track them visually"