: Research is underway with academic partners like Nanyang Technological University to build a multi-agent execution layer on the XRPL. This would allow developers to deploy task-specific agents, such as trading bots and IoT services, directly on the ledger. CBDCs and the Private Ledger
: These models enable On-Demand Liquidity (ODL) to scale efficiently, delivering transactions at the optimal cost and passing those savings back to customers.
Ripple is actively integrating and Artificial Intelligence (AI) across its ecosystem to optimize liquidity and secure the XRP Ledger (XRPL) for institutional use cases like Central Bank Digital Currencies (CBDCs) . Machine Learning on RippleNet : Research is underway with academic partners like
Ripple utilizes ML specifically to address the complex problem of for its customers.
: As of early 2026, AI is being integrated to bolster XRPL's reliability as it scales for global payments and tokenized assets . : ML models predict global customer demand on
: ML models predict global customer demand on a daily and long-term basis to determine exactly how much liquidity is needed, where, and when.
Ripple’s is built on a private ledger that utilizes the core energy-efficient technology of the public XRPL. AI and Security for Developers
: Some ML models are already in pre-production, making critical business decisions that drive faster transactions and 24/7 global availability. AI and Security for Developers