The software landscape has transformed due to the emergence of agentic AI, which features intelligent, autonomous applications capable of decision-making and continuous interaction. While much focus is on Python and JavaScript, Java is becoming pivotal for developing scalable, production-grade agentic AI applications. Java stands out for its excellent concurrency support, robustness, security, and integration capabilities, making it ideal for complex AI applications that require orchestration across APIs and databases. Key open-source projects such as the Model Context Protocol (MCP), LangChain4j, Quarkus, and OpenTelemetry (OTel) are driving this shift within the Java ecosystem. MCP provides a runtime for long-lived agents; LangChain4j facilitates LLM orchestration; Quarkus optimizes Java for cloud-native applications; and OTel enhances observability. For Java developers, embracing these tools now is crucial for participating in the agentic AI revolution while leveraging existing Java development expertise. Agentic AI is not just for startups; it’s here to stay.
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