Ripple Launches XRPL Proxy Payment Toolkit for AI-Driven Automated Transactions

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Ripple has released an XRPL Proxy Payment Toolkit to enable AI-driven automated transactions, according to AI + crypto news. The toolkit allows developers to build applications where AI agents can manage payments, invoices, and settlements without human intervention. Ripple emphasized the need for infrastructure featuring fast and predictable on-chain settlement. The x402 protocol supports XRP and RLUSD. Robinhood and MetaMask are also developing AI agent integrations for financial transactions.

ChainCatcher reports that Ripple has launched a developer toolkit for building "agent payments" applications on the XRP Ledger (XRPL), enabling AI agents to execute automated financial transactions. Ripple states that AI agents are no longer a futuristic concept but are already actively participating in paying for computational power, settling invoices, and completing transactions without human intervention. As AI agent applications expand, the market is accelerating the development of machine-oriented payment infrastructure—including wallets and stablecoin payment channels—that allow AI to autonomously pay for services and trade assets. This week, Robinhood also announced a program allowing users to experiment with AI agents executing stock trades, with plans to expand into crypto assets in the future; MetaMask has similarly released a non-custodial wallet solution tailored for AI agents. Ripple notes that traditional payment systems are primarily designed for human-initiated and approved processes, whereas AI agents require infrastructure capable of fast settlement, predictable outcomes, and no human approval—and emphasizes that its new toolkit supports payment capabilities based on the x402 protocol, enabling settlements with XRP and Ripple USD (RLUSD). Meanwhile, the IC3 team, composed of researchers from multiple universities, states that although AI combined with blockchain enables automated transactions, AI agents remain highly dependent on humans and underlying infrastructure and are not fully autonomous.

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