> For the complete documentation index, see [llms.txt](https://docs.402gate.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.402gate.com/ecosystem-and-rewards/user-and-ai-agent-rewards.md).

# User & AI Agent Rewards

#### **Overview**

To promote adoption among real users and autonomous AI systems, 402Gate includes **cashback and rebate systems** tied directly to usage volume.

#### **Reward Mechanics**

1. **Usage Cashback**\
   Users receive up to **5% rebate** in $402G on cumulative payments made within a 7-day period.
2. **AI Agent Rebates**\
   AI agents that perform consistent automated payments (e.g., >1000 microtransactions/week) are eligible for **performance rewards**.
3. **Referral Credits**\
   Users inviting other developers or agents to the protocol earn a referral bonus from transaction fees of the invitee’s endpoints.

#### **Example Scenario**

* An AI trading bot makes 5000 micropayments for data feeds.
* The agent receives **2% back in $402G** as cashback.
* The API provider also receives **revenue share** from the same activity.

This creates a **closed-loop economic cycle** that continuously benefits all sides consumers, providers, and the network.

***

### **6.4 Partner & Provider Revenue Sharing**


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.402gate.com/ecosystem-and-rewards/user-and-ai-agent-rewards.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
