> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vivi.bot/llms.txt
> Use this file to discover all available pages before exploring further.

# Agents

**Agents** are the core of the VIVI platform. An agent is your virtual team member that interacts with customers, employees, or systems. Agents combine several components, such as knowledge bases and prompts, into one system. These components allow your agent to deliver helpful, accurate, and efficient support. At a minimum, every agent requires a name, prompt, and model to be live. Once configured, you can use the **Test Agent** feature or **Evaluations** to validate responses before deployment.

Unlike traditional decision trees or scripted bots, VIVI agents are **dynamic** — they reason, act, and adapt to context. Agents can be duplicated, customized, or scaled as your organization evolves.

***

## Building Agents

<video autoPlay muted loop playsInline className="w-full aspect-video rounded-xl" src="https://mintcdn.com/ksg-ea85c748/Vks9o3B-pSxqY0Lp/videos/vivi_agent_components.mp4?fit=max&auto=format&n=Vks9o3B-pSxqY0Lp&q=85&s=13d0b68108399e47139bba7e19a636ab" data-path="videos/vivi_agent_components.mp4" />

When building your agent, you can customize every detail to match your organization's standards, tone, and goals.

<Steps>
  <Step title="Choose a Name & Description">
    Choose a clear name and description that reflects the agent's purpose.
  </Step>

  <Step title="Add a Prompt">
    Define the agent's role, tone, and rules of engagement.
  </Step>

  <Step title="Select a Model">
    Choose the large language model that powers the agent. See [Models](/documentation/core-concepts/agents/models) for a full breakdown of available options.
  </Step>

  <Step title="Connect Knowledge Bases">
    Provide the internal sources of truth the agent can reference.
  </Step>

  <Step title="Enable Integrations">
    Connect MCP servers, APIs, or even other agents for advanced actions.
  </Step>

  <Step title="Add Skills">
    Add custom built skills to your agent to allow it to run code locally.
  </Step>

  <Step title="Enable Citations">
    Allow the agent to include numbered references to knowledge base sources in its responses. See [Citations](/documentation/core-concepts/agents/citations) for setup details.
  </Step>

  <Step title="Enable Structured Output">
    Force the agent to reply with a typed JSON object instead of free-form text. Useful for API-driven workflows. See [Structured Output](/documentation/core-concepts/agents/structured-output) for details.
  </Step>

  <Step title="Select Channel">
    Decide where the agent will be active (web chat, WhatsApp, API, etc.).
  </Step>

  <Step title="Test the Agent">
    Use the testing features to verify accuracy and performance before deployment.
  </Step>
</Steps>

***

## Best Practices

* **Use descriptive names** so team members can quickly identify each agent's role.
* Choose large-context models for complex reasoning and smaller, faster models for lightweight workflows.
* **Enable citations** when your agent draws from knowledge bases — it builds user trust and makes responses easier to verify.
* **Use structured output only for API-driven workflows** where a downstream system needs to parse the agent's response as JSON.
* **Test thoroughly** after configuring each component to ensure accuracy and reliability.