Agentic AI on JAVAPRO International

LangChain4j Agentic Workflows: From AI Calls to Multi-Agent Systems in Java

Wed, 08 Jul 2026 07:00:02 +0000

You have built AI features into your Java application. Your model is wrapped in a service, RAG is feeding it context, tools are wired, and calls are flowing. It works. Then requirements evolve. A single prompt-and-response is no longer enough. You need steps that follow each other, branches based on decisions, retries when things fail, and multiple actions running concurrently. The question shifts from “how do I call an LLM?” to “how do I orchestrate multiple LLM-driven tasks into a coherent system?”

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.

Building MCP Tools (for AI Agents) using Spring AI

Wed, 07 Jan 2026 07:00:02 +0000

Introduction

The innovation of AI Agents and Agentic AI systems has revolutionized the adoption of Generative AI. Agents empower systems to autonomously achieve goals by performing tasks. Tools are the key building blocks for AI agents that provide them with superior decision-making and implementation capabilities. Model Context Protocol (MCP) protocol has standardized the approach of building tools enabling faster, seamless integration. In this paper, we are going to talk about MCP, the underlying architecture. We will also provide a simple code example of building MCP tools in Java using Spring AI and integrating it with an AI Agent.