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

# ERC-AI Agent Standard

### A New AI Digital Asset Class <a href="#id-3.-erc-ai-agent-token-standard-a-new-ai-digital-asset-class" id="id-3.-erc-ai-agent-token-standard-a-new-ai-digital-asset-class"></a>

The ERC-AI Agent Token Standard introduces an entirely new category of digital assets, enabling on-chain AI ownership, validation, and monetization. AI models become verifiable, secure, and fully interoperable across blockchains.

**Core Innovations:**

* AI Agents as Tokenized Assets: AI models are issued as ERC-AI tokens, ensuring transparency and security.
* On-Chain Execution & Validation: Smart contract-based proof-of-training and AI integrity.
* Cross-Chain Operability: AI agents remain functional and verifiable across Ethereum, Avalanche, Arbitrum, and more.
* Decentralized Licensing & Monetization: Developers and users can trade or license AI models securely.

Authorship & Development:

Led by iAgent, co-authored by LayerZero, Sequence, and 4 CTOs from top-tier Web3 infrastructure projects.

**Why This Standard is a Breakthrough:**

* Turns AI models into tradable, ownable digital assets LLM , VLM and other core architectures.
* Creates a scalable framework for decentralized AI execution.
* Paves the way for AI monetization and ownership in Web3.

<figure><img src="/files/bHW5nMV3O1ygjcWSQLwS" alt=""><figcaption><p>MarketHub</p></figcaption></figure>


---

# 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.iagentpro.com/product-and-ecosystem/erc-ai-agent-standard.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

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.
