build-test-suite

name	build-test-suite
description	Build a complete test suite with test set and test cases for evaluating an AI agent. Guides through test set type selection, scenario design using vertical-specific templates, expected behavior crafting, and bulk creation. Use when user says "create test cases", "build test suite", "add test scenarios", "set up evaluation tests", or "design test cases".
argument-hint	[agent-name-or-use-case]

Build Test Suite

Guide the user through building a complete test suite — test set + test cases with expected behaviors — for evaluating an AI agent using the coval CLI. Follow the phases below in order, asking questions at each step.

If $ARGUMENTS contains an agent name or use case, use it to skip or pre-fill questions in Phases 1-2.

Phase 0: Setup + Preflight

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 agents list --format json
coval test-sets list --format json

Note existing agents and test sets for reference throughout the flow.

Phase 1: Agent Context

Ask: "Which agent are these tests for?"

• If agents exist, present a numbered list and let the user pick or say "new"
• If $ARGUMENTS matches an agent name, select it automatically

Fetch the selected agent's details:

coval agents get <agent_id> --format json

Capture from the response:

• agent_id
• model_type (voice, chat, etc.)
• prompt (system prompt, if available)
• display_name

If the agent has a system prompt, use it later to generate more specific, domain-relevant test scenarios instead of generic templates.

Phase 2: Test Set Type Selection

Load references/test-set-types.md and present the available types.

Ask: "What type of test set do you want to create?"

• SCENARIO is the default and best for most use cases
• Explain when each type is appropriate based on the reference
• If the user is unsure, recommend SCENARIO

Note: Test set type is not configurable via the CLI — all test sets default to SCENARIO type. To create other types, use the API: POST /v1/test-sets with a test_set_type field.

Then ask:

1. "What would you like to name this test set?" — suggest: "<Agent Name> Evaluation"
2. "Brief description?" — suggest based on agent type and use case

Create the test set:

coval test-sets create --name "<name>" --description "<desc>" --format json

Capture test_set_id from the JSON response.

Phase 3: Scenario Design

Load references/test-case-templates.md and select the templates matching the agent's vertical/use case.

Present the 3-category pattern:

• happy_path — The standard, successful interaction
• edge_case — Unusual or challenging situations
• compliance — Regulatory, policy, or safety requirements

If the agent has a system prompt, customize the scenarios to be specific to the agent's domain rather than using generic templates. For example, if the agent handles dental appointments, tailor scenarios to dental-specific situations.

Present a summary table before creating:

Test Set: "<name>"

  [happy_path]   <test case name>
                 <scenario description>
  [edge_case]    <test case name>
                 <scenario description>
  [compliance]   <test case name>
                 <scenario description>

Ask: "Create these test cases? (yes / customize / add more)"

• yes → proceed to Phase 4
• customize → let the user edit scenarios, then re-present
• add more → generate additional scenarios, then re-present

Phase 4: Expected Behaviors

For each test case, help craft an expected_behaviors array. These are what the Composite Evaluation metric scores against.

Good expected behaviors are:

• Specific — describes a concrete action or output
• Observable — can be verified from the conversation transcript
• Binary — it either happened or it didn't

Examples of GOOD expected behaviors:

• "Agent verifies caller identity before sharing account details"
• "Agent provides a confirmation number"
• "Agent offers at least two alternative time slots"
• "Agent does NOT share information from a different policy"

Examples of BAD expected behaviors (avoid these):

• "Agent is helpful" — too vague
• "Agent sounds nice" — subjective
• "Agent handles the situation well" — not observable

Present each test case with its expected behaviors for confirmation. Let the user add, remove, or edit behaviors.

Phase 5: Bulk Creation

Create each test case:

coval test-cases create \
  --test-set-id <test_set_id> \
  --input '<scenario text>' \
  --expected "Agent greets the customer professionally" \
  --expected "Agent verifies caller identity" \
  --expected "Agent resolves the issue or escalates" \
  --description "<test case name>" \
  --format json

Pass each expected behavior as a separate --expected flag. This ensures they are stored as individual items in the expected_behaviors array, which the Composite Evaluation metric scores individually.

Shell tip: Use single quotes for --input values to avoid shell interpolation issues (e.g., $45.99 becoming .99).

If the CLI does not support multiple --expected flags, use the Coval API directly for structured expected behaviors:

curl -s -X POST https://api.coval.dev/v1/test-cases \
  -H "X-API-Key: $COVAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "test_set_id": "<test_set_id>",
    "input_str": "<scenario text>",
    "expected_behaviors": [
      "Agent greets the customer professionally",
      "Agent verifies caller identity",
      "Agent resolves the issue or escalates"
    ],
    "description": "<test case name>"
  }'

Present progress as each test case is created. Capture test_case_id from each response.

Phase 6: Coverage Summary + Next Steps

Present what was created:

Test Suite Complete!

  Test Set:     <name> (<test_set_id>)
  Test Cases:   <N> total
    [happy_path]   <count>
    [edge_case]    <count>
    [compliance]   <count>

Coverage Analysis

Review the test cases and suggest areas that might need more coverage:

• Are there common failure modes not covered?
• Are there regulatory requirements specific to the vertical?
• Would the agent benefit from multi-turn conversation tests?
• Are there language/accent scenarios worth testing (for voice agents)?

Suggested Next Steps

• Design a test persona: /design-persona
• Configure evaluation metrics: /configure-metrics
• Launch a quick evaluation: /quick-eval
• Add more test cases later:

  coval test-cases create --test-set-id <test_set_id> --input "..." --expected "..." --description "..."