Spring AI on JAVAPRO International

Solving Spring AI's UI Challenge with AG-UI's Java SDK

Thu, 16 Jul 2026 07:00:01 +0000

Introduction: The Missing Link in Java AI Development

Ask any Java developer who has tried to build an AI feature in a production application and they’ll tell you the same thing: the backend isn’t the hard part anymore. Spring AI makes it easy to invoke an LLM, define prompts, and create tools. But turning these interactions into a smooth, interactive user experience? That’s where the struggle begins.

Most developers solve this by inventing custom message formats, WebSocket events, and ad-hoc rules for how the UI should render tool calls. Each application reinvents the bridge between frontend interactions and agent intelligence. The result? A fragile, inconsistent layer that must be rebuilt for every project.

From Prototype to Production: Building Safe and Reliable AI Agents with Spring AI and MCP

Thu, 25 Jun 2026 07:00:01 +0000

The AI agent demo works perfectly in the development environment. Your prototype handles natural language queries, executes tool calls, and generates sensible responses. But when stakeholders ask ‘Can we deploy this?’, reality sets in. That question exposes the massive gap between a functional prototype and a Spring AI production system ready for real traffic, unexpected failures, and business-critical operations.

That question reveals the massive gap between a working prototype and a production system. The demo doesn’t handle failures gracefully. It has no guardrails against harmful outputs. There’s no visibility into what the agent is actually doing. Cost per request? Unknown. Error rates? No idea. Recovery strategy when the LLM provider goes down? Haven’t thought about it.

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].

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.