Java Web Development (JSP/Servlets) Services

As agentic AI shifts from prototypes to enterprise production, Java emerges as a powerful alternative to Python-centric stacks. This article looks into building robust agentic applications using LangChain4j for orchestration, Quarks for high-performance deployment, Model Context Protocol (MCP) for standardized tool and data access, and OpenTelemetry for comprehensive observability. Through practical code examples — including tool definitions, agent creation with memory, RAG integration, and production patterns — the guide demonstrates Java's advantages in type safety, low-latency execution, deep system integration, and audit-ready tracing. This is ideal for developers seeking scalable, reliable agentic solutions in mission-critical environments.

Agentic AI — autonomous systems that reason, plan, use tools, remember context, and execute complex multi-step tasks — is moving from experimental prototypes to production workloads in enterprises. While Python ecosystems (LangChain, LlamaIndex, CrewAI) led the early wave, Java is emerging as a serious contender for mission-critical agentic applications.

Modern APIs often need to support dynamic filtering, sorting, and pagination without creating dozens of custom endpoints. One of the most widely used standards for this is OData (Open Data Protocol). OData has established itself as a powerful standard for building and consuming RESTful APIs. It provides a uniform way to query and manipulate data, offering clients unparalleled flexibility through system query options like $filter, $select, and $expand.

Example: 

When working on modern software, a developer will often use hundreds or thousands of dependencies. Кeeping an accurate and consistent bill of materials is essential for license compliance and for security.

Motivation

In a large organization, the scope of dependencies review given by build-time scanning has some limitations.

Enterprise Java applications still serve business-critical processes but are becoming vulnerable to changing security threats and regulatory demands. Traditional compliance-based security methods tend to respond to audits or attacks, instead of stopping them. This paper introduces a risk-based security architecture, which focuses on protection according to the impact of the business, the probability of the threat, and exposure. The threat modeling, dependency risk analysis, and layered security controls help organizations to minimize the attack surfaces beforehand without impacting on performance and delivery velocity. The strategy is explained with the help of real-life examples of enterprise Java to facilitate its use in practice.

Intended Audience

The audience targeted in the article is those an enterprise architect, senior Java developer, security architect, and DevSecOps teams who are required to design, modernize or secure large-scale Java applications. In recent years, there are a number of breaches of enterprises that have not been initiated by a zero-day exploit but a known vulnerability, which has not been prioritized e.g. an outdated library, an open API, or a poorly configured integration In a number of instances, the organizations were technically compliant but still exposed because of the homogenous, checklist-driven security measures that did not concentrate on the high-risk elements. 

Memory tuning in Java has evolved over years and whenever each version was released, we anticipate some magic. If you worked with Java 6 or 7, you probably remember spending hours tweaking PermGen, experimenting with CMS flags, and nervously watching GC logs in production. But with Java 25, Memory Optimization and Utilization are more mature.

Modern Java gives us better garbage collectors, improved container awareness, stronger tooling, and smarter runtime ergonomics. But despite all that progress, memory optimization is something that you can't ignore. In a cloud-native environment where every gigabyte costs money, memory efficiency directly affects both performance and money spent on infrastructure as well.

In this article I am trying to summarize some of the best practices for memory utilization, so developers can use it as a reference guide.

1. Start with Measurement, Not Assumptions

The most common mistake that we could usually see is increasing heap size without understanding allocation patterns. A bigger heap often delays a problem rather than solving it.