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Documentation Index

Fetch the complete documentation index at: https://docs.coval.dev/llms.txt

Use this file to discover all available pages before exploring further.

Each agent configuration acts as a reusable connection profile that can be referenced across multiple simulations, evaluations, and conversations without requiring reconfiguration.

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How to Configure Agents

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Adding an Agent

Connect Agent Demo
  1. Navigate to the Agents section in your dashboard
  2. Click “Add New Agent”
  3. Configure the connection parameters:
    • Endpoint URL: API endpoint for your agent service
    • Phone Number: For voice-based agents requiring telephony access
    • Authentication: API keys or authentication tokens as required
  4. Set operational parameters:
    • Language Preferences: Primary and fallback language configurations
    • Agent Behavior Prompts: System prompts or behavioral guidelines
    • Simulator Types: Compatible simulation environments

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Attributes

In your agents, you can set specific attributes associated with that agent. For example, if you have multiple agents representing different restaurant reservation services, you could define the attributes such as “opening_hours” and “menu_items”. You can embed these agent attributes into test case scenarios or metric prompts by inserting {{agent.attribute_name}}. In the example above, you could create a metric that asks: 
Did the agent give the correct opening hours?  
Opening hours are `{{agent.opening_hours}}`
or, if you could use it in a test case: 
Order two items from this list: {{agent.menu_items}}

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Workflow

Each agent can have a workflow — a visual graph that maps out the conversation steps and decision points your agent is designed to handle. Workflows help you document intended behavior, identify gaps in test coverage, and communicate how your agent should move through a conversation. See Workflow for details on creating, generating, and editing workflows.

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Knowledge Base

Each agent can have a knowledge base — a set of reference documents (FAQs, policies, product docs) that LLM metrics can use as a source of truth when evaluating your agent’s responses. Attaching accurate reference content lets Coval catch hallucinations, contradictions, and gaps that transcript-only evaluation can miss. See Knowledge Base for details on supported source types, how to add entries, and how to enable KB context on metrics.

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Test Your Connection

Once your agent is configured and saved, use the Test connection button in the agent config page to verify connectivity before running simulations. What gets tested:
  • WebSocket agents — Coval performs the WebSocket handshake and reports whether the connection was established successfully.
  • HTTP / OpenAI-compatible agents — Coval sends the initialization request and reports the response.
Reading the result:
  • Success — A green confirmation appears and auto-dismisses after 60 seconds.
  • Failure — A red alert stays visible with the error details. Expand the result to see the raw JSON response for debugging.
The button is disabled when you have unsaved changes. Save first, then test.

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Connect Your Agent

Inbound Voice

Receive incoming phone calls for customer service scenarios

Outbound Voice

Make calls to users for sales and scheduling

OpenAI Endpoint

Connect OpenAI-compatible chat APIs

Chat WebSocket

Text chat over persistent WebSocket connections

OpenAI Realtime

Voice-to-voice agents on the OpenAI Realtime API

Gemini Live

Voice-to-voice agents on Google Gemini Live

Pipecat Cloud

Integrate with Pipecat Cloud agents

LiveKit

Advanced real-time communication platform