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MoonPay launches non-custodial wallets for AI agents

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MoonPay launches non-custodial wallets for AI agents

Crypto payments platform MoonPay has introduced a new product designed to give artificial intelligence systems direct access to digital wallets and on-chain transactions.

Summary

  • MoonPay launched MoonPay Agents on to support non-custodial AI wallets.
  • The platform enables automated trading, funding, and machine-to-machine payments.
  • The product targets developers building large-scale autonomous financial systems.

MoonPay Agents, a non-custodial software layer that enables AI agents to create wallets, manage funds, and trade on behalf of verified users, was officially launched by the company on Feb. 24.

The system is built on MoonPay’s command-line interface and is aimed at developers building automated programs that need to move money without relying on centralized custody. Once a user completes identity checks and funds a wallet, an AI agent can trade, swap, and transfer assets independently.

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Connecting AI systems to digital money

MoonPay said the product supports the full financial cycle, including fiat-to-crypto funding, portfolio tracking, and conversion back to traditional currencies. Users can also receive funds through virtual accounts or payment services such as Apple Pay, PayPal, and Venmo.

“AI agents can reason, but they cannot act economically without capital infrastructure,” said Ivan Soto-Wright, the company’s chief executive officer. He said the goal is to make crypto the default financial layer for autonomous systems.

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According to MoonPay, users can set up a working wallet and agent connection in minutes, allowing automated systems to begin executing strategies almost immediately.

MoonPay Agents includes tools such as recurring purchases, real-time cross-chain swaps, machine-to-machine payments, and automated fiat funding via on-ramps. These features are designed to ensure that agents always have access to liquidity when operating.

Additionally, the platform supports portfolio monitoring, token discovery, and basic risk analysis, enabling developers to incorporate financial management straight into their apps. Wallets are stored on users’ own devices, giving them direct control over private keys.

The product is built to scale from single-user setups to networks of thousands of agents. It runs on the same infrastructure that supports nearly 500 enterprise customers and more than 30 million users across 180 countries.

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Part of a the growing “agent economy” trend

The launch comes amid growing interest in so-called “agentic” systems that can plan and act without continuous human oversight. Industry forecasts suggest the autonomous agent economy could reach $30 trillion by 2030, with AI systems managing a large share of routine financial decisions.

In crypto markets, this shift is already underway. AI-powered wallets are being used for trading, DeFi activity, and machine-to-machine payments. At ETHDenver 2026, developers showcased blockchain-based identity tools, automated treasuries, and agent-led trading systems, highlighting the rapid growth of this trend.

According to company executives, MoonPay Agents will serve as a default financial rail for developers building trading bots, gaming platforms, and automated payment systems. With AI systems increasingly taking on financial tasks, MoonPay is positioning its infrastructure as a foundation for this emerging market.

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Crypto World

Tether Unveils AI System to Run Large Models on Smartphones

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Tether Unveils AI System to Run Large Models on Smartphones

Tether, issuer of the world’s largest stablecoin by market cap, USDT, has released a new AI training framework that it says allows large language models to be fine-tuned on consumer hardware, including smartphones and non-Nvidia GPUs.

According to Tuesday’s announcement, the system, part of its QVAC platform, uses Microsoft’s BitNet architecture and LoRA techniques to reduce memory and compute requirements, potentially lowering the cost and hardware barriers to developing AI models.

The framework supports cross-platform training and inference across a range of chips, including AMD, Intel and Apple Silicon, as well as mobile GPUs from Qualcomm and Apple.

Tether said its engineers were able to fine-tune models with up to 1 billion parameters on smartphones in under two hours, and smaller models in minutes, with support extending to models as large as 13 billion parameters on mobile devices.

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