Coval Platform Resource Overview

Reference for $ARGUMENTS. Use this to understand Coval's resource model before making API calls or exploring data.

Resource Hierarchy

All resources are scoped to your organization (determined by your API key).

Agent (22-char ID)
└── Mutation (26-char ID)
Test Set (8-char ID)
└── Test Case (22-char ID)
Persona (22-char ID)
Metric (22-char ID)
Run (22-char ID)
└── Simulation (22-char ID)
    └── Metric Output (26-char ID)
Run Template (22-char ID)
└── Scheduled Run (22-char ID)

Resources

Agent

An AI system being evaluated. Represents the customer's agent configuration.

• ID format: 22-char string
• Key fields: customer_agent_id (unique per org), display_name, model_type, phone_number, endpoint, metadata, prompt, language, test_set_ids, metric_ids, attributes
• Model types: MODEL_TYPE_VOICE, MODEL_TYPE_OUTBOUND_VOICE, MODEL_TYPE_CHAT, MODEL_TYPE_WEBSOCKET, MODEL_TYPE_API, MODEL_TYPE_ENDPOINT
• Connection types: Inbound voice, outbound voice, OpenAI endpoint, Pipecat, LiveKit, WebSocket
• API: GET/POST /v1/agents, GET/PATCH/DELETE /v1/agents/{agent_id}
• CLI: coval agents list|get|create|update

Mutation

A named configuration variant of an agent for A/B testing. Config overrides are deep-merged with the base agent's metadata at runtime.

• ID format: 26-char string
• Key fields: display_name, description, config_overrides (deep-merged JSON), parameter_values (flattened for display), status (ACTIVE/DELETED)
• Relationship: Belongs to an Agent via agent_id
• Unique constraint: One active mutation per (agent_id, display_name)
• API: GET/POST /v1/agents/{agent_id}/mutations, GET/PATCH/DELETE /v1/agents/{agent_id}/mutations/{mutation_id}
• CLI: coval mutations list|get

Test Set

A collection of test cases that define WHAT to test (scenarios, expected behaviors).

• ID format: 8-char string
• Key fields: display_name, slug, description, test_set_type, parameters (JSON template variables)
• Test set types: DEFAULT, SCENARIO, TRANSCRIPT, AUDIO, IVR, SCRIPT
• API: GET/POST /v1/test-sets, GET/PATCH/DELETE /v1/test-sets/{test_set_id}
• CLI: coval test-sets list|get|create|update

Test Case

An individual test scenario within a test set.

• ID format: 22-char string
• Key fields: input_str (the scenario/prompt), input_type, expected_behaviors (array of strings), expected_output_json, description, simulation_metadata_input
• Input types: SCENARIO (natural language task), TRANSCRIPT (OpenAI format conversation), AUDIO (pre-recorded file), SCRIPT (ordered lines), IVR, MANUAL
• Relationship: Belongs to a Test Set
• API: GET/POST /v1/test-cases, GET/PATCH/DELETE /v1/test-cases/{test_case_id}
• CLI: coval test-cases list|get|create|update

Persona

Defines HOW the simulated user behaves during testing (voice, personality, interruption style).

• ID format: 22-char string
• Key fields: display_name, simulated_user_prompt, voice_name, language_code, avatar_url, metadata
• Persona controls: Voice selection, background noise, interruption rate (NONE/LOW/MEDIUM/HIGH), silent mode, caller phone number
• Relationship: Referenced by Runs and Run Templates
• API: GET/POST /v1/personas, GET/PATCH/DELETE /v1/personas/{persona_id}
• CLI: coval personas list|get|create|update

Metric

Definition of an evaluation criterion and how to score it.

• ID format: 22-char string
• Key fields: metric_name (globally unique slug), display_name, description, metric_metadata (JSON config), output_type, category
• Output types: FLOAT, STRING, SET, BOOLEAN
• Features: Built-in metrics, custom LLM-based prompting, metric chaining, human review integration, expected-behavior evaluation
• API: GET/POST /v1/metrics, GET/PATCH/DELETE /v1/metrics/{metric_id}
• CLI: coval metrics list|get|create|update

Run

Top-level execution entity. Launches simulations of test cases against an agent.

• ID format: 22-char string
• Key fields: agent_id, persona_id, test_set_id, display_name, status, config (JSON), is_monitoring, tags, customer_metadata, scheduled_run_id
• Statuses: PENDING, IN QUEUE, IN PROGRESS, COMPLETED, FAILED, CANCELLED, DELETED
• Run types: Simulation run (is_monitoring=false) vs monitoring/conversation run (is_monitoring=true)
• Config options: iteration_count (1-50), concurrency (1-100), sub_sample_size, mutation_ids
• Total simulations = test_cases x iterations x (1 + mutation_count)
• API: GET/POST /v1/runs, GET/DELETE /v1/runs/{run_id}
• CLI: coval runs list|get|launch|watch|delete

Simulation

Result of running a single test case against an agent (one per test_case x iteration x variant).

• ID format: 22-char string
• Key fields: run_id, test_case_id, iteration, mutation_id, mutation_name, transcript, status, audio_length_seconds, external_conversation_id, tool_calls
• Mutation tracking: mutation_id and mutation_name identify which Mutation variant was used. Both are null for base agent simulations (no mutation).
• API: GET /v1/simulations, GET/DELETE /v1/simulations/{simulation_id}, GET /v1/simulations/{simulation_id}/audio
• CLI: coval simulations list|get|audio|delete
• Filtering: Supports mutation_id, mutation_name, agent_id, run_id, status, test_case_id, external_conversation_id, create_time

Metric Output

Result of evaluating a single simulation with a specific metric.

• ID format: 26-char string
• Key fields: metric_output_id, metric_id, value (float, string, or array), status
• Relationship: Child of Simulation, references a Metric
• API: GET /v1/simulations/{simulation_id}/metrics, GET /v1/simulations/{simulation_id}/metrics/{metric_output_id}

Run Template

Reusable, saved run configuration for scheduling.

• ID format: 22-char string
• Key fields: display_name, agent_id, persona_id, test_set_id, metric_ids, mutation_ids, run_config (JSON source of truth)
• Soft deletion: status=ACTIVE or DELETED
• API: GET/POST /v1/run_templates, GET/PATCH/DELETE /v1/run_templates/{id}

Scheduled Run

Cron/rate-based schedule that triggers runs from a template.

• ID format: 22-char string
• Key fields: template_id, schedule_expression (cron or rate), timezone, enabled
• Examples: rate(15 minutes), cron(0 2 * * ? *)
• API: GET/POST /v1/scheduled_runs, GET/PATCH/DELETE /v1/scheduled_runs/{id}

ID Formats Quick Reference

Resource	Length	Example
Agent	22	camudk3VhC3kmuvutXKLvF
Mutation	26	01KJ6N707FD9YEPKBSGX1KCW5V
Test Set	8	a1275ab2
Test Case	22	ac67b2c8916f41b6974084
Persona	22	nKexF9ZUt19tLtb3ZQsqzG
Metric	22	hUWu3PxY6G7fTYbLzBAwZm
Run	22	KNNxeP6Vfxx83K4TncVPLH
Simulation	22	LKuChNCLArtF8MLhBix5xY
Metric Output	26	01JCQR8Z9PQSTNVWXY123456
Run Template	22	abc123xyz789def456ghi0
Scheduled Run	22	xyz789abc123def456ghi0

Common Workflows

Launch an evaluation

1. Identify Agent, Test Set, Persona (list with CLI or API)
2. POST /v1/runs with agent_id, test_set_id, persona_id, optional mutation_ids
3. Poll GET /v1/runs/{run_id} or use coval runs watch {run_id}
4. Results: GET /v1/simulations?filter=run_id="{run_id}" then fetch metrics per simulation

Compare mutations (A/B test)

1. Create mutations: POST /v1/agents/{agent_id}/mutations with config_overrides
2. Launch run with mutation_ids array
3. List simulations: GET /v1/simulations?filter=run_id="{run_id}"
4. Group by mutation_id (null = base agent)
5. Look up mutation names: GET /v1/agents/{agent_id}/mutations
6. Compare metrics across groups

Schedule recurring evaluations

1. Create Run Template with agent, persona, test set, metrics, mutations
2. Create Scheduled Run referencing the template with a cron/rate expression
3. Runs auto-launch and reference back via scheduled_run_id

Submit live conversations (monitoring)

1. POST /v1/conversations:submit with conversation data
2. Creates a Run with is_monitoring=true and single Simulation
3. Metrics evaluated in real-time

API Conventions

• Base URL: https://api.coval.dev/v1
• Auth: X-API-Key header
• Pagination: page_size (1-1000, default 50), page_token, response includes next_page_token
• Filtering: AIP-160 syntax — filter=status="COMPLETED" AND agent_id="abc123"
• Sorting: order_by=-create_time (prefix - for descending)

Tools & References

• OpenAPI Spec: GET https://api.coval.dev/v1/openapi — always fetch the latest spec before building integrations
• CLI: https://github.com/coval-ai/cli — install with brew install coval-ai/tap/coval
• MCP Server: https://github.com/coval-ai/mcp-server — Model Context Protocol server for LLM tool access
• Docs: https://docs.coval.dev