AI Agents Overview

AI Agents allow you to automate customer conversations using large language models. When a customer sends a message to your sender, the agent can respond intelligently, answer questions from your knowledge base, and execute actions via webhooks.

When to Use AI Agents

AI Agents are ideal for:

Use Case	Description
Customer Support	Answer common questions, troubleshoot issues, and escalate complex cases
Lead Qualification	Collect contact information and qualify leads through conversation
Appointment Booking	Guide customers through scheduling via conversational flows
FAQ Answering	Provide instant answers using your knowledge base documents
Order Status	Look up orders and provide updates via webhook integrations

Supported Providers

Configure your agent with any of these LLM providers:

Provider	Models	Notes
OpenAI	gpt-4o, gpt-4o-mini, gpt-4-turbo	Most popular, great general-purpose
Anthropic	claude-3-5-sonnet, claude-3-5-haiku	Excellent reasoning capabilities
Google	gemini-1.5-pro, gemini-1.5-flash	Cost-effective for high volume
Mistral	mistral-large, mistral-small	European data residency option
Zavu	Managed models	No API key needed, billed to your account

Using the Zavu provider means we handle the LLM API calls for you. Costs are charged directly to your Zavu balance at pass-through rates with no markup.

Quick Start

Here’s how to create an AI agent for a sender:

import Zavudev from "@zavudev/sdk";

const zavu = new Zavudev({
  apiKey: process.env["ZAVUDEV_API_KEY"],
});

// Create an agent for your sender
const agent = await zavu.senders.agent.create("sender_abc123", {
  enabled: true,
  provider: "openai",
  model: "gpt-4o-mini",
  systemPrompt: `You are a helpful customer support agent for Acme Corp.
Be friendly and concise. If you don't know the answer, say so.
For billing questions, ask the customer to email billing@acme.com.`,
  temperature: 0.7,
  contextWindowMessages: 10,
  triggerOnChannels: ["sms", "whatsapp"],
});

console.log("Agent created:", agent.id);

import os
from zavudev import Zavudev

zavu = Zavudev(
    api_key=os.environ.get("ZAVUDEV_API_KEY"),
)

# Create an agent for your sender
agent = zavu.senders.agent.create(
    "sender_abc123",
    enabled=True,
    provider="openai",
    model="gpt-4o-mini",
    system_prompt="""You are a helpful customer support agent for Acme Corp.
Be friendly and concise. If you don't know the answer, say so.
For billing questions, ask the customer to email billing@acme.com.""",
    temperature=0.7,
    context_window_messages=10,
    trigger_on_channels=["sms", "whatsapp"],
)

print(f"Agent created: {agent.id}")

require "zavudev"

client = Zavudev::Client.new(api_key: ENV["ZAVUDEV_API_KEY"])

agent = client.senders.agent.create("sender_abc123",
  enabled: true,
  provider: "openai",
  model: "gpt-4o-mini",
  system_prompt: "You are a helpful customer support agent for Acme Corp.\nBe friendly and concise. If you don't know the answer, say so.\nFor billing questions, ask the customer to email billing@acme.com.",
  temperature: 0.7,
  context_window_messages: 10,
  trigger_on_channels: ["sms", "whatsapp"]
)

puts "Agent created: #{agent.id}"

package main

import (
	"context"
	"fmt"
	"os"

	"github.com/zavudev/sdk-go"
)

func main() {
	client := zavudev.NewClient(zavudev.WithAPIKey(os.Getenv("ZAVUDEV_API_KEY")))

	agent, _ := client.Senders.Agent.Create(context.TODO(), "sender_abc123", zavudev.AgentCreateParams{
		Enabled:               zavudev.Bool(true),
		Provider:              zavudev.String("openai"),
		Model:                 zavudev.String("gpt-4o-mini"),
		SystemPrompt:          zavudev.String("You are a helpful customer support agent for Acme Corp.\nBe friendly and concise. If you don't know the answer, say so.\nFor billing questions, ask the customer to email billing@acme.com."),
		Temperature:           zavudev.Float(0.7),
		ContextWindowMessages: zavudev.Int(10),
		TriggerOnChannels:     []string{"sms", "whatsapp"},
	})

	fmt.Printf("Agent created: %s\n", agent.ID)
}

$client = new Zavudev\Client(apiKey: getenv('ZAVUDEV_API_KEY'));

$agent = $client->senders->agent->create('sender_abc123', [
    'enabled' => true,
    'provider' => 'openai',
    'model' => 'gpt-4o-mini',
    'systemPrompt' => "You are a helpful customer support agent for Acme Corp.\nBe friendly and concise. If you don't know the answer, say so.\nFor billing questions, ask the customer to email billing@acme.com.",
    'temperature' => 0.7,
    'contextWindowMessages' => 10,
    'triggerOnChannels' => ['sms', 'whatsapp'],
]);

echo "Agent created: {$agent->id}\n";

curl -X POST https://api.zavu.dev/v1/senders/sender_abc123/agent \
  -H "Authorization: Bearer $ZAVU_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "enabled": true,
    "provider": "openai",
    "model": "gpt-4o-mini",
    "systemPrompt": "You are a helpful customer support agent for Acme Corp.\nBe friendly and concise. If you dont know the answer, say so.\nFor billing questions, ask the customer to email billing@acme.com.",
    "temperature": 0.7,
    "contextWindowMessages": 10,
    "triggerOnChannels": ["sms", "whatsapp"]
  }'

Agent Architecture

When a message arrives, the agent processes it through this flow:

                    Inbound Message
                          |
                          v
                  +---------------+
                  | Agent Enabled?|
                  +---------------+
                    |           |
                   No          Yes
                    |           |
                    v           v
              +---------+  +-----------------+
              | Webhook |  | Check Flow State|
              |  Only   |  +-----------------+
              +---------+       |         |
                          Has Active   No Active
                             Flow        Flow
                               |           |
                               v           v
                         +---------+  +-----------+
                         | Execute |  | Match Flow|
                         |   Step  |  |  Triggers |
                         +---------+  +-----------+
                               |           |
                               |      No Match
                               |           |
                               v           v
                         +---------+  +-----------+
                         |  Send   |  | Query KB  |
                         |Response |  | + LLM Call|
                         +---------+  +-----------+
                                           |
                                           v
                                     +-----------+
                                     |   Send    |
                                     |  Response |
                                     +-----------+

Key Components

LLM Configuration

Choose your AI provider and model. Configure system prompts, temperature, and context window size.

Conversational Flows

Create structured conversation paths for lead capture, booking, and multi-step processes.

Webhook Tools

Let the AI execute actions by calling your webhooks (check orders, create tickets, etc.).

Knowledge Base

Upload documents and FAQs. The agent uses RAG to find relevant context when answering.

Pricing

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

Provider	Model	Input Cost	Output Cost	~Cost per Message
OpenAI	gpt-4o-mini	$0.15/1M tokens	$0.60/1M tokens	~$0.0002
OpenAI	gpt-4o	$2.50/1M tokens	$10/1M tokens	~$0.003
Anthropic	claude-3-5-haiku	$0.25/1M tokens	$1.25/1M tokens	~$0.0003
Anthropic	claude-3-5-sonnet	$3/1M tokens	$15/1M tokens	~$0.005
Google	gemini-1.5-flash	$0.075/1M tokens	$0.30/1M tokens	~$0.0001

For most use cases, gpt-4o-mini or gemini-1.5-flash offer the best balance of quality and cost. Reserve larger models for complex reasoning tasks.

Next Steps

Setup Guide

Configure your first AI Agent step-by-step

Conversational Flows

Build structured conversation paths

Webhook Tools

Add custom actions via webhooks

Knowledge Base

Set up RAG with your documents