> ## Documentation Index
> Fetch the complete documentation index at: https://docs.zavu.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# AI Agents Overview
> Automate customer conversations with intelligent AI-powered agents
# 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:
```typescript TypeScript theme={null}
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);
```
```python Python theme={null}
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}")
```
```ruby Ruby theme={null}
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}"
```
```go Go theme={null}
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)
}
```
```php PHP theme={null}
$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";
```
```bash cURL theme={null}
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
Choose your AI provider and model. Configure system prompts, temperature, and context window size.
Create structured conversation paths for lead capture, booking, and multi-step processes.
Let the AI execute actions by calling your webhooks (check orders, create tickets, etc.).
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
Configure your first AI Agent step-by-step
Build structured conversation paths
Add custom actions via webhooks
Set up RAG with your documents