Every major software wave added new business capabilities. AI’s real impact will come when it powers adaptive, intelligent business systems — not just faster development.
Static analysis for LLM agents that flags prompt-injection risks—like confused deputy flows and dynamic prompts—before runtime, improving security and auditability.
RAG answers can stay stable while evidence shifts. Learn why evidence stability matters for reproducibility, auditability, and debugging — and how to check it.
Moving a hardcoded LangGraph React agent into LaunchDarkly AI Configs so prompts, models, tools, tracking, and rollout testing can be changed without redeploying.
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.
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