> For the complete documentation index, see [llms.txt](https://docs.maiagent.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.maiagent.ai/maiagent-user-guide/maiagent-user-guide-en/conversations/webhook.md).

# Webhook

## Purpose

Developers need to provide an API endpoint (essentially a URL) where the system can send data. When the MaiAgent AI Assistant generates a reply on a chat platform, those replies are automatically sent to the Webhook endpoint you configured.

This setup allows you to integrate AI responses into your own systems — for example, logging them, forwarding them to users, or using them for other applications.

All Webhook delivery records are centrally displayed on this page for easy viewing, tracking, and troubleshooting.

{% hint style="info" %}
[How to integrate via API?](/tech/api-integration/quickstart.md)
{% endhint %}

<figure><img src="/files/kNVV7RtDRnouxH77BMSj" alt=""><figcaption></figcaption></figure>

## Use Cases

### **Customer Service System Integration**

After the AI Assistant answers a user's question, use Webhooks to sync the reply content to your enterprise customer service system, allowing support agents to view conversation records, follow up, or take over manually.

### **Real-Time Notification Push**

Push AI Assistant replies or specific events (such as anomalies) to communication tools like Slack, LINE Notify, or Email to alert relevant personnel for immediate action.

### **Data Analysis and Record Archiving**

Automatically send all AI conversation data to a data warehouse, cloud database, or Google Sheets for usage behavior analysis, quality assessment, or interaction record archiving.

### **Internal Process Automation**

When users submit issues, request resources, or report anomalies through the AI Assistant, Webhooks can automatically route this data to internal form systems or task management tools (such as Jira or Notion) for follow-up tracking.


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