AWS Bedrock with C++ libraries helps Ripple shorten the diagnosis time of XRP Ledger

Network operation engineers for the XRP Ledger often spend days tracing issues, starting from petabytes of logs generated by C++ libraries. Ripple and Amazon Web Services are testing the use of Amazon Bedrock AI to reduce this process to just a few minutes by enabling the system to automatically analyze and link these logs to the XRPL source code.

Challenges in monitoring the XRP Ledger at scale and complexity

The XRP Ledger functions as a decentralized layer-1 network with over 900 nodes distributed across universities and businesses worldwide. Each validator server uses a C++ platform to support high throughput but also creates a major problem: each node produces 30–50 GB of logs, totaling approximately 2–2.5 petabytes across the entire network.

When anomalies occur, engineers typically need C++ experts to interpret these logs and trace back to the C++ libraries where the root cause lies. This process is slow, requires deep expertise, and prolongs incident response times. For example, when the Red Sea submarine cable event affected Asia-Pacific connectivity, teams had to process massive log files before they could begin investigating the root cause.

Bedrock AI: transforming raw logs into understandable signals

AWS architect Vijay Rajagopal introduced a new approach: Bedrock acts as a transformation layer, converting raw log data into searchable and analyzable signals. Instead of just viewing raw C++ lines, engineers can query AI models to check network behavior against expected standards.

The process begins by transferring node logs into Amazon S3 via GitHub and AWS Systems Manager. Then, events trigger AWS Lambda functions to determine segmentation boundaries for each file. Metadata for these segments is pushed into Amazon SQS for parallel processing, speeding up the analysis.

Another Lambda function retrieves relevant byte ranges from S3, extracts log lines and metadata, and forwards them to CloudWatch for indexing. AWS also describes a parallel workflow to create and update XRPL source code documentation, using EventBridge to monitor key repositories and store snapshots by version.

Linking logs with source code and C++ libraries: a quick problem-solving key

The most critical aspect of the solution is the ability to link logs with software versions and XRPL specifications. Relying solely on logs is insufficient to understand protocol edge cases. By combining log tracing with corresponding C++ libraries, server software, and technical standards, AI agents can map anomalies to precise code paths.

This approach is vital because it allows engineers to quickly understand why errors occur within validator C++ libraries. Instead of manually sifting through hundreds of lines of code, AI can automatically pinpoint exactly which part needs adjustment.

Practical results: from days to minutes

Internal assessments shared by AWS staff indicate that some incident reviews can be reduced from several days to just 2–3 minutes. This improvement enables XRPL operators to respond faster to performance degradations or service disruptions.

This work takes place as the XRP Ledger ecosystem expands token features through Multi-Purpose Tokens and releases Rippled 3.0.0 with new patches and modifications. Optimizing monitoring processes will support these developments.

Current status and next steps

Currently, the collaboration between Ripple and AWS remains in the research phase and has not yet been publicly deployed. No official release date has been announced, and teams are still verifying the accuracy of the AI models and data governance during log analysis.

It also depends on node operators choosing which data to share during incident investigations. However, this approach demonstrates that AI and cloud tools can effectively support blockchain observation and operation without altering XRPL consensus rules or compromising network security.