"The modern enterprise seeks intelligent, adaptive systems capable of sophisticated planning, context-aware decision-making, and leveraging massive real-world data. This blog describes an example that achieves this by combining agentic AI using Embabel and Spring AI with the power of semantic and structural queries using YugabyteDB as the backbone, while applying enterprise Java best practices to predict flight delays in a robust, scalable manner."
"Java remains the gold standard for enterprise applications. Its stability, performance, and extensive ecosystem have been proven in mission-critical workloads. Stability and Scalability: Java's extensive ecosystem and strict typing have long served as the foundation for large-scale, mission-critical applications. Robust Tooling: Enterprise-grade libraries, mature frameworks, and strong ecosystem enable complex workflows and reliable integration touchpoints. Performance: JVM optimizations and multi-threading capabilities deliver fast processing, even with heavy data and compute loads."
Modern enterprises require intelligent, adaptive systems capable of sophisticated planning, context-aware decision-making, and leveraging massive real-world data. An indicative workflow combines agentic AI via Embabel and Spring AI with semantic and structural queries powered by YugabyteDB, applying enterprise Java best practices to predict flight delays in a scalable, robust manner. Java provides stability, scalability, robust tooling, and performance through JVM optimizations and multi-threading. Agentic AI overcomes static workflows by enabling goal-directed software agents that adapt to changing information and dynamic environments. The implementation omits some production parameters but demonstrates a workflow for enterprise-grade GenAI applications using proven Java frameworks and database infrastructure.
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