Java Web Development (JSP/Servlets) Services

Modern Java applications require robust observability and automated intelligence to ensure reliability at scale. Dynatrace’s AI-driven platform continuously learns application behavior, establishes statistical baselines and applies deterministic, causation based analysis to detect anomalies and pinpoint root causes.

By correlating metrics, logs, traces, and topology context across applications, services and infrastructure, Dynatrace can automatically highlight the true source of problems and assess their impact. This drastically reduces alert noise and MTTR .

Building real-time applications means balancing user responsiveness with heavy backend processing. A proven solution is to decouple heavy workloads using events and asynchronous processing. In this approach, a Spring Boot application quickly publishes events to Kafka instead of processing requests inline. Then Kafka consumers (with AI/ML logic) handle the data in the background, and the results are pushed to clients in real time via WebSockets. This article highlights three key patterns enabling this architecture:

1. Event Production with Spring Boot and Kafka
2. AI-Driven Processing in Kafka Consumers
3. Real-Time WebSocket Delivery to the Frontend

Event Production with Spring Boot and Kafka

The first step is capturing an event and publishing it to Kafka. By offloading work to Kafka the application can respond immediately to the user without waiting for processing. Spring Boot’s integration with Apache Kafka provides a KafkaTemplate to send messages to topics.

PUT requests are typically used for updating an existing resource. This means replacing the current data for the target resource with the data sent in the API request body.

Just like POST requests, the content-type header is important because it tells the server how to interpret the data we’re sending.

Java has always been a serious language for production systems, and in 2026, the Generative AI ecosystem has finally caught up. For years, Java developers watched from the sidelines as Python and TypeScript accumulated framework after framework for building LLM-powered applications. Today, the picture is very different. Java has multiple mature, actively maintained AI frameworks, each with its own philosophy and trade-offs.

This article covers the four frameworks I have personally used to ship Java AI applications: Genkit Java, Spring AI, LangChain4j, and Google ADK Java. Each one represents a meaningfully different bet on what a Java AI framework should be, and understanding those differences will save you from picking the wrong tool.

Recently at IBM Software Labs, I worked on a task that forced me to understand something many Java developers rarely think about — how Java interacts with the operating system.

Most of our daily work happens safely inside the JVM. Memory management, threads, and file handling — the JVM abstracts these away nicely.