My goal here is to experiment with an alternative approach leveraging Java's tried-and-tested, robust functionalities that have been available since JDK 1.5.
Agentic Agile Office uses autonomous AI agents to cut admin overhead, detect risks early, and shift teams from manual tracking to intelligent, high-velocity delivery.
MuleSoft IDP uses AI to extract and structure data from documents like invoices and PDFs, helping automate workflows, reduce errors, and improve processing speed.
Jakarta EE 12 introduces the Data Age of Enterprise Java with Jakarta Query, improved data access, and a unified model for cloud-native and polyglot systems.
LLM-powered deep parsing converts messy industrial inventory data into structured, searchable data, enabling precise searches and scalable deduplication.
Achieve zero-downtime deployments for Java applications on Kubernetes using rolling updates, readiness/liveness probes, and graceful shutdown strategies.
AI agents have access, move at machine speed, and raise no alarms. Your DLP was built for humans — by the time it flags risk, the data is already gone.
An AI-native analytics agent sits between users and the data warehouse, translating natural-language questions into governed SQL or Python workflows and dashboards.
This article explains how an AI Gateway centralizes LLM access, enabling secure routing, governance, cost control, and visibility for scalable AI adoption.
Design a stateless JWT auth service with Spring Boot 3, Redis caching, and Sentinel for high availability, faster token validation, and reduced DB load.
Feature flags help teams move fast, but when they’re not cleaned up, they quietly add extra code, slow down performance, and make applications harder to maintain.
Transitioning AI agents from POC to production requires moving beyond permissive access to a zero-trust architecture. This covers the essential security layers.
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