Building Production-Ready AI Agents with Java and Spring AI

Thu, 30 Apr 2026 07:00:01 +0000

Java developers have been building enterprise applications for decades, but when it comes to AI, the conversation has been dominated by Python. Spring AI changes this situation. It brings the same patterns we know from Spring - dependency injection, auto-configuration, portable abstractions - to AI development.

In January 2026 we built a sample agent application, and we want to share what we learned. The sample AI agent handles conversations with memory, answers questions from internal knowledge bases, uses external APIs, and integrates with existing microservices - all running on Amazon Bedro ck. If you want to try it yourself, the Building AI Agents with Java and Spring AI workshop [1] walks you through typical challenges of Generative AI models and provides step by step solutions with code available on GitHub [2].

Java Performance Optimization with Agentic AI: Autonomous Diagnostics and Actionable Recommendations

Tue, 07 Apr 2026 07:00:01 +0000

Java performance diagnosis in production is manual, slow, and requires deep expertise. You get a Grafana alert, grab a thread dump, download a JFR recording, open it in JDK Mission Control, stare at flamegraphs, correlate with metrics — hours of work per incident. Most teams don’t have a dedicated performance engineer, so alerts get ignored or result in generic “add more replicas” responses.

We built a system that does this autonomously. When a monitoring alert fires, it collects profiling data and thread dumps, extracts runtime metrics, generates flamegraphs, and sends everything to an AI model that produces a structured performance report — including root cause analysis and concrete code fixes with file paths and line numbers from the actual source repository. The pipeline runs on Kubernetes, triggered by Grafana webhooks, with results stored in Amazon S3.

Bezsonov Yuriy on JAVAPRO International

Building Production-Ready AI Agents with Java and Spring AI

Java Performance Optimization with Agentic AI: Autonomous Diagnostics and Actionable Recommendations