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AI Agents

An AI Agent is an automated responder attached to a sender that uses large language models (LLMs) to handle incoming messages. When a customer sends a message, the agent processes it and generates an intelligent response.

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What is an AI Agent?

Think of an AI Agent as a virtual assistant for your messaging. It can:
  • Answer customer questions using your knowledge base
  • Collect information through conversational flows
  • Execute actions via webhook integrations
  • Escalate to humans when needed
{
  "id": "agent_abc123",
  "name": "Support Agent",
  "enabled": true,
  "provider": "openai",
  "model": "gpt-4o-mini",
  "systemPrompt": "You are a helpful customer support agent for Acme Corp...",
  "contextWindowMessages": 10,
  "triggerOnChannels": ["sms", "whatsapp"],
  "stats": {
    "totalInvocations": 1250,
    "totalTokensUsed": 450000,
    "totalCost": 0.85
  }
}

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How Agents Work

When a message arrives, the agent processes it through several stages: 
Inbound Message (SMS/WhatsApp)
        |
        v
   Agent Enabled?
        |
    +---+---+
    |       |
   No      Yes
    |       |
    v       v
 Webhook   Check Active Flow
  Only          |
            +---+---+
            |       |
        Has Flow   No Flow
            |       |
            v       v
      Continue   Check KB
        Flow    for Context
            |       |
            v       v
      Execute   Generate
        Step    Response
            |       |
            +---+---+
                |
                v
         Send Response

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Processing Flow

  1. Check for active flow: If the contact is in a conversational flow, continue that flow
  2. Match flow triggers: Check if the message should start a new flow (keywords, intent)
  3. Retrieve context: Get conversation history and relevant knowledge base chunks
  4. Generate response: Call the LLM with context and system prompt
  5. Send response: Deliver the response via the same channel

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Key Components

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LLM Configuration

Configure which AI model powers your agent:
SettingDescription
ProviderOpenAI, Anthropic, Google, Mistral, or Zavu (managed)
ModelThe specific model (e.g., gpt-4o-mini, claude-3-5-sonnet)
System PromptInstructions that define the agent’s personality and behavior
TemperatureCreativity level (0 = deterministic, 2 = creative)
Max TokensMaximum response length
Context WindowNumber of previous messages to include

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Flows

Flows are deterministic conversation paths for structured interactions like lead capture or appointment booking. They combine fixed messages, data collection, and AI-generated responses. 
[Welcome] → [Ask Name] → [Ask Email] → [Confirm] → [End]
Flows are useful when you need:
  • Guaranteed data collection (name, email, phone)
  • Consistent messaging for compliance
  • Step-by-step processes (booking, ordering)

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Tools

Tools allow your agent to execute actions by calling your webhooks. The LLM can decide when to use a tool based on the conversation.
Example ToolUse Case
check_order_statusLook up order tracking info
create_ticketOpen a support ticket
book_appointmentSchedule a calendar event
get_inventoryCheck product availability

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Knowledge Base

A Knowledge Base stores documents that the agent can reference when answering questions. Using RAG (Retrieval Augmented Generation), the agent searches for relevant content and includes it in its context.
  • Upload FAQs, product docs, policies
  • Automatic chunking and embedding
  • Semantic search for relevant context

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Pricing

AI Agent costs are pass-through with no markup. You pay exactly what the AI providers charge:
ProviderModelInputOutput~Cost/Message
OpenAIgpt-4o-mini$0.15/1M$0.60/1M~$0.0002
OpenAIgpt-4o$2.50/1M$10/1M~$0.003
Anthropicclaude-3-5-haiku$0.25/1M$1.25/1M~$0.0003
Anthropicclaude-3-5-sonnet$3/1M$15/1M~$0.005
Googlegemini-1.5-flash$0.075/1M$0.30/1M~$0.0001
If you use the Zavu provider, we handle the API key and charge your account balance directly.

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Best Practices

Write Clear Prompts

Your system prompt defines the agent’s behavior. Be specific about tone, limitations, and when to escalate.

Use Flows for Structure

For data collection or multi-step processes, use flows instead of relying on the LLM to remember steps.

Add Knowledge Base

Upload FAQs and product docs so the agent can provide accurate, specific answers.

Monitor Costs

Check execution logs regularly. Use smaller models like gpt-4o-mini for cost efficiency.

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Next Steps

Setup Guide

Configure your first AI Agent

Flows Guide

Build conversational flows

Tools Guide

Add webhook integrations

Knowledge Base

Set up RAG with your documents