AI NewsRipple Brings Wallet-Based AI Agent Payments to XRP Ledger

Ripple Brings Wallet-Based AI Agent Payments to XRP Ledger

Ripple has partnered with Privy, a Stripe-owned company, to bring AI-driven payments and embedded wallets to the XRP Ledger in a move that expands agentic commerce.

The deal points to a future where software agents pay for services without manual approval.

How the XRP Ledger Fits Into AI Payments

Agentic commerce describes purchases that AI agents make on a user’s behalf, including payments for data, computing power, and software. The XRP Ledger is a public blockchain that settles payments in XRP and Ripple’s RLUSD stablecoin.

This collaboration lets developers build payment apps using XRP, RLUSD and other assets, without requiring users to manage private keys.

Privy’s programmable wallets handle wallet creation, user approvals, and embedded crypto payments inside applications. Building on Ripple’s AI Starter Kit, released in June, the partnership helps developers build autonomous payment apps.

Central to the deal is x402, an open payment protocol built for automated software-to-software transactions. An AI agent could buy data access, settle the payment on XRPL and receive the service instantly.

Ripple says the agent payments are already significant. About 14 million have been processed on XRPL, about 25% of network activity, up from 1 million in July.

Most of these payments involve small amounts, which highlights adoption rather than value.

Can AI Agents Make the XRP Ledger a Settlement Layer?

The partnership could position the XRP Ledger as a core settlement layer for machine-to-machine commerce. Such features could make automated payments easier for businesses to manage. Even so, the price effect is uncertain.

XRP traded near $1.37 on October 13, within a range of $1.31 to $1.38. The token gained roughly 0.31% over the last day, giving it a market cap near $86.7 billion.

Any sustained move will depend on broader market conditions and on further adoption of these tools. For developers, the key question is whether these tools attract real commercial use beyond early experiments. Adoption figures will be a useful gauge.

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