Amazon Web Services and Ripple are reportedly researching the use of Amazon Bedrock’s generative artificial intelligence (Gen-AI) capabilities to improve how the XRP Ledger is monitored and analyzed, according to people familiar with the initiative.
The tech giant and crypto company are planning to apply AI analysis to the XRP Ledger’s system logs to reduce the time needed to investigate network issues. Some internal assessments from AWS engineers indicate that processes that once took several days could now be completed in 2-3 minutes.
XRPL is a decentralized layer-1 blockchain supported by a global network of independent node operators. The system has been live since 2012 and is built on the C++ code, a computational choice that makes it fast but generates complex system logs.
XRPL seeks to cut operational demands with Amazon Bedrock
According to Ripple’s documents, XRPL operates more than 900 nodes distributed globally in universities, blockchain institutions, wallet providers, and financial firms. The decentralized setup improves its resilience, security, and scalability, but complicates visibility into how the network behaves in real time.
Each node produces between 30 and 50 gigabytes of log data, resulting in an estimated 2 to 2.5 petabytes of data. When incidents occur, engineers must manually sift through these files to identify anomalies and trace them back to the underlying C++ code.
A single investigation could stretch to about two or three days because it requires platform engineers and a limited pool of C++ experts who understand the protocol’s internals to closely coordinate. Platform teams found themselves waiting on engineers before they could respond to incidents or resume feature development, amplified by the age and size of the codebase.
According to AWS technicians speaking at a recent conference, a Red Sea subsea cable cut once affected connectivity for some node operators in the Asia-Pacific region. Ripple’s platform team had to collect logs from affected operators, then process tens of gigabytes per node before a meaningful analysis could begin.
Solutions architect at AWS Vijay Rajagopal said the managed platform that hosts artificial intelligence agents, also known as Amazon Bedrock, is capable of reasoning over large datasets. Using Bedrock on XRPL’s log analysis would supposedly automate pattern recognition and behavioral analysis to cut down time taken by manual inspectors.
According to Rajagopal, Amazon Bedrock is an interpretive layer between raw system logs and human operators. It can help scan cryptic entries line by line, and engineers could query AI models that understand the structure and expected behavior of the XRPL system.
AWS Bedrock log processing and code analysis pipeline
Rajagopal also talked about the technical workflow, starting with the raw logs generated by validators, hubs, and client handlers of XRPL. The logs are first transferred into Amazon S3 through a dedicated workflow using GitHub tools and AWS Systems Manager.
Once the data reaches S3, event triggers activate AWS Lambda functions that inspect each file to determine byte ranges for individual chunks in tandem log line boundaries and predefined chunk sizes.
The resulting segments are then sent to Amazon SQS to distribute the processing at scale. A separate log processor Lambda function retrieves only the relevant chunks from S3 based on the metadata it receives. It then extracts log lines and associated metadata before forwarding them to Amazon CloudWatch, where they can be indexed and analyzed.
“It actually retrieves only the relevant chunks from S3 based on the configured chunk metadata that it read. And it passes the log lines, gets the metadata out of it, and puts these log lines and metadata to CloudWatch,” the architect explained.
Away from the log ingestion solution, the system also processes the XRPL codebase with two primary repositories. One contains the core server software for the XRP Ledger, while the other defines standards and specifications for interoperability with apps built on top of the network.
Updates from these repositories are automatically detected and scheduled through a serverless event bus called Amazon EventBridge. On a defined cadence, the pipeline pulls the latest code and documentation from GitHub, versions the data, and stores it in S3 for further processing.
The AWS engineers claimed that without understanding how the protocol is supposed to behave, raw logs may not be enough to solve node problems and downtimes. They propounded that by linking logs to the standards and server software that define XRPL’s behavior, AI agents can provide more accurate explanations of anomalies.
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