How do you monitor your JVM applications effectively? One powerful option is JDK Flight Recorder (JFR). JFR makes troubleshooting and profiling easier by capturing detailed records of JVM events, and its streaming API lets you access this data in real-time. But what if we could take this a step further by streaming live JFR data from your JVM application directly into an AI system to enhance monitoring and troubleshooting or even prevent potential issues before they occur?

In this session, we’ll demonstrate how to use JFR to build self-improving applications with the help of AI and the latest JDK features. Using a real-life simulated example, you’ll learn how to:

Capture and stream JFR data in real time,
Integrate JVM event data to an AI-based system,
Spot performance bottlenecks and unusual behavior automatically,
Build better tools for debugging and monitoring.
By the end of this talk, you’ll have a clear roadmap for combining JFR and AI to enhance the troubleshooting experience and observability of your JVM applications.

Jfokus

Oracle JVM engineers demonstrate an intelligent monitoring system for Java microservices that combines JDK Flight Recorder (JFR), Java Management Extensions (JMX), and AI. A Java agent is deployed to each microservice to stream JFR events to a central monitoring service. JMX MBeans expose runtime controls (like enabling batch processing or clearing caches) with metadata describing available actions. An AI service built with LangChain4j and Claude uses those discovered actions to build structured prompts, analyzes incoming JFR data for anomalies, and autonomously invokes JMX operations when confidence thresholds are met. The system includes human-in-the-loop confirmation for critical actions and per-service conversation memory to track trends over time.

Intelligent JVM Monitoring: Combining JDK Flight Recorder with AI, by Yagmur Eren