Amazon Bedrock will revolutionize response times on XRP Ledger: from days to minutes

Amazon Web Services and Ripple are taking concrete steps to transform how the XRP Ledger network is monitored and analyzed. The goal is ambitious: reduce what currently takes days of investigation in incidents to just two or three minutes through artificial intelligence. This change could make a critical difference in how operators respond to connectivity failures or anomalies in the network.

The challenge: massive volumes of logs in a decentralized architecture

XRP Ledger operates as a layer 1 decentralized network with over 900 nodes distributed globally across universities, companies, and service providers. The technical complexity lies in the fact that each of these nodes, built on a C++ codebase, generates between 30 and 50 GB of logs daily. In total, the network accumulates approximately 2 to 2.5 petabytes of log data.

When an incident occurs—such as the submarine cable cut in the Red Sea that affected operators in Asia-Pacific—technical teams face a bottleneck: they need C++ specialists to trace anomalies down to the protocol code. This dependency significantly slows response times to performance degradations or outages.

The solution: an AI-powered data pipeline

The approach Ripple and AWS are exploring combines native AWS tools with Bedrock analysis capabilities. The flow begins when node logs are transferred to Amazon S3 via integrations with GitHub and AWS Systems Manager.

Once ingested, event triggers activate Lambda functions that segment each file into manageable chunks. The metadata of these chunks is sent to Amazon SQS for parallel processing. Another Lambda function extracts relevant byte ranges from S3 and forwards the data to CloudWatch, where it is indexed for quick searches.

This distributed system is critical: without it, engineers would have to manually process massive files before even starting a root cause review.

Linking intelligence with technical specifications

What sets this solution apart is that it not only analyzes logs; it also versions XRPL code and standards documentation. AWS monitors key repositories and stores versioned snapshots in S3. During an incident, the system pairs a log signature with the correct software version and the corresponding specification.

This linkage is essential because isolated logs may not reveal a protocol edge case. By correlating traces with server software and specifications, AI agents can map anomalies to probable code paths. The result is faster, more consistent guidance for operators during outages.

Growth context for XRPL

The work comes at a time when the XRP Ledger ecosystem is expanding its capabilities. XRPL has introduced Multi-Purpose Tokens, a design focused on efficiency and simplified tokenization. Ripple has also published amendments and fixes in Rippled 3.0.0, expanding the network’s operational surface.

With a current price of $1.94 USD and a market capitalization of $117.63 billion, XRP Ledger is a critical infrastructure that demands world-class observability tools.

Current status and next steps

For now, this effort remains in research and testing phases. Neither company has announced a public deployment date, and teams are still validating model accuracy and data governance. Success will also depend on what information node operators voluntarily share during investigations.

However, the approach demonstrates how AI and cloud tools can significantly improve blockchain observability without modifying XRPL’s consensus rules. If successful, this model could become a standard for decentralized networks facing similar scale and technical complexity challenges.