Java Web Development (JSP/Servlets) Services

The recent announcement that Global Payments will acquire Worldpay for $22.7 billion has once again put the spotlight on the payment processing space. This move consolidates two giants and signals the growing importance of real-time, global payment infrastructure. But behind this shift is something deeper: data streaming has become the backbone of modern payment systems.

From Stripe’s 99.9999% Kafka availability to PayPal streaming over a trillion events per day, and Payoneer replacing its existing message broker with data streaming, the world’s leading payment processors are redesigning their core systems around streaming technologies. Even companies like Worldline, which developed its own Apache Kafka management platform, have made Kafka central to their financial infrastructure.

Agentic AI is changing how enterprises think about automation and intelligence. Agents are no longer reactive systems. They are goal-driven, context-aware, and capable of autonomous decision-making. But to operate effectively, agents must be connected to the real-time pulse of the business. This is where data streaming with Apache Kafka and Apache Flink becomes essential.

Apache Flink is entering a new phase with the proposal of Flink Agents, a sub-project designed to power system-triggered, event-driven AI agents natively within Flink’s streaming runtime. Let’s explore what this means for the future of agentic systems in the enterprise.

Most engineers imagine failures as sudden events. A service crashes. A node goes down. An alert fires, and everyone jumps into action. In real high-availability Java systems, failures rarely behave that way. They almost always arrive quietly first.

Systems that have been running reliably for months or years begin to show small changes. Latency creeps up. Garbage collection pauses last a little longer. Thread pools spend more time near saturation. Nothing looks broken, and dashboards stay mostly green. Then one day, the system tips over, and the failure suddenly looks dramatic.

Instead of building custom integrations for a variety of AI assistants or Large language models (LLMs) you interact with — e.g., ChatGPT, Claude, or any custom LLM — you can now, thanks to the Model Context Protocol (MCP), develop a server once and use it everywhere. 

This is exactly as we used to say about Java applications; that thanks to the Java Virtual Machine (JVM), they're WORA (Write Once Run Anywhere). They're built on one system and expected to run on any other Java-enabled system without further adjustments.

Many Kotlin codebases are written by developers with a Java background. The syntax is Kotlin, but the mindset is often still Java, resulting in what can be called "Java with a Kotlin accent." This style compiles and runs, but it misses the core advantages of Kotlin: conciseness, expressiveness, and safety.

Common symptoms include: