AI Agents

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.

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

Example Agent ConfigurationCopy
{
  "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
  }
}

How Agents Work

When a message arrives, the agent processes it through several stages:

CodeCopy
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

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 (keyword, always, and intent — the last judged by a classifier rather than by matching words; see Flows)
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

Key Components

LLM Configuration

Configure which AI model powers your agent:

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.

CodeCopy
[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)

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.

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

Pricing

AI Agent costs are pass-through with no markup. You pay exactly what the AI providers charge:

~Cost/Message assumes roughly 1,000 input and 150 output tokens. Provider prices move: the agent's model picker in the dashboard lists the catalog currently in force, and is what billing charges against.

Best Practices

Next Steps