Bedrock AI helps optimize XRP Ledger monitoring tools through C++ library processing

XRP Ledger is a decentralized layer-1 network with over 900 independent nodes worldwide, but monitoring and analyzing system issues is becoming increasingly complex. The biggest challenge comes from C++ libraries used in the XRPL infrastructure generating enormous log volumes, slowing down diagnosis and troubleshooting. To address this, Amazon Web Services (AWS) and Ripple are testing Amazon Bedrock — an advanced AI platform — to speed up log analysis from several days to just 2-3 minutes.

Challenge with Massive C++ Logs on the XRPL Network

The XRP Ledger ledger operates on a C+±based platform designed for high throughput. However, this also means each node on the network produces an enormous amount of logs — from 30 to 50 GB per node, totaling about 2 to 2.5 PB across the entire network. Processing this data typically requires engineers with deep C++ expertise to trace errors back to the core protocol code.

Previously, when issues occurred, root cause analysis could take days or even longer. Internal assessments shared by AWS staff indicate that handling large log files from different nodes and finding correlations is time-consuming and error-prone. An illustrative example is the Red Sea submarine cable outage in 2026, when some nodes in the Asia-Pacific region were affected, requiring engineers to collect logs from multiple operators before starting investigations.

Automated Data Processing Solution from AWS

Ripple and AWS are developing a technology pipeline to automate the entire XRPL log processing workflow. The process begins with logs from validators and servers being transferred into Amazon S3 via GitHub tools and AWS Systems Manager. Upon receiving the data, event triggers activate AWS Lambda functions to determine segment boundaries for each file.

Metadata for these segments is pushed into Amazon SQS for parallel processing, enhancing performance. Another Lambda function then retrieves relevant byte ranges from S3, extracts log lines along with metadata, and forwards them to CloudWatch for indexing. This entire process is orchestrated by EventBridge, enabling efficient large-scale log processing. With this infrastructure, AWS reports that log analysis time has been significantly reduced compared to previous manual methods.

Integrating XRPL Source Code and Standards into the AI System

A key factor making Amazon Bedrock effective is its ability to link log signals with C++ source code and XRPL protocol standards. According to a presentation by AWS architect Vijay Rajagopal, the system will monitor repositories containing XRPL source code, schedule updates via Amazon EventBridge, and store versioned snapshots in S3.

When an anomaly is detected, Bedrock can associate a log signature with the corresponding software release and specifications. This is crucial because raw logs alone are insufficient to explain protocol edge cases. By combining log traces with server software and standards, AI agents can map anomalies to precise code paths in C++ libraries. As a result, node operators receive faster, more consistent guidance during disruptions or performance degradations.

Project Outlook and Current Status

Currently, the collaboration between AWS and Ripple remains in the research and testing phase. No official deployment date has been announced, and teams are still validating the accuracy of the AI models and data governance processes. Additionally, deployment depends on node operators’ willingness to share log data.

Meanwhile, this effort coincides with ongoing XRPL feature expansions. Ripple recently released Rippled 3.0.0 with significant updates and patches, and is preparing to announce Multi-Purpose Tokens (XLS-86) — a versatile token design aimed at efficiency and easier tokenization. Nonetheless, this AI and cloud-based approach shows promising potential to enhance blockchain observability without altering XRPL’s fundamental consensus rules.