AI-Powered Software Engineering Beyond the Hype

Smarter decisions, high-performance code, and real-world AI in practice.

Live on October 07, 2026 | 10:15 - 13:15

Tage

Stunden

Minuten

Sekunden

🇬🇧 English

Better Decisions, Faster Systems, AI Powered Solutions

AI is changing software engineering far beyond code generation. From architectural decision making and high performance code to AI powered troubleshooting, this Fullstack Live Event brings together three practical sessions from JAX London. Discover how experienced engineers use AI and proven engineering practices to make smarter decisions, build faster systems, and solve real world problems.

From Better Decisions to AI Powered Engineering

The impact of AI depends on how thoughtfully it is integrated into the engineering process. Explore how to make better technology and architecture decisions, how to use generative AI safely in performance critical environments, and how to build production ready AI agents for troubleshooting. Across three sessions, you will gain practical approaches that can be applied directly to modern software projects.

Your expert sessions

10:15 - 10:45 | Worth a Million Arguments | Venkat Subramaniam

Explore practical approaches to making better technology and architecture decisions. The session examines common engineering debates and shows how learning from experienced practitioners can lead to more effective, informed decisions.

11:15 - 12:00 | Low Latency Machine – Can Generative AI Write High-Performance Code? | Mark Price

Can AI generate code that meets the demands of latency sensitive systems? Learn where generative AI can support expert engineers, where it fails under real workloads, and how prompting, agent design, and validation can improve AI generated code.

16:00 – 16:45 CEST | Small Signals, Big Incidents: Catching Production Problems in the Performance Lab with JFR & AI | Ram Lakshmanan

You run stress tests. You run endurance tests. Response times, throughput, CPU & memory all look healthy. Yet problems still surface in production. Most production problems don’t appear suddenly. Their early signals were already in the performance lab, just too small for broad metrics & static thresholds to catch. In this session, you will learn how to use JFR (Java Flight Recorder) to capture the micro metrics that reveal these signals. You will also see how AI can analyze JFR recordings, correlate signals & pinpoint deviations before they become production incidents.

Expert Knowledge for

Software Architects, who want to make better technology and architecture decisions
Software Engineers, who want to use AI effectively in their development workflows
Performance Engineers, who need reliable approaches to AI generated high performance code
Java Developers, who want to build AI powered troubleshooting solutions

Join us and learn how to...

Make better architecture and technology decisions based on proven engineering practices
Evaluate and validate AI generated code in performance critical systems
Use effective prompting and agent design for high performance development
Build a Troubleshooting AI Agent with Java and LangChain4j
Combine RAG, embeddings, vector databases, tools, and conversational memory for production ready AI applications