4 posts tagged with "JAAP"

Over the past decades, development tools have continuously evolved in programming capabilities, yet orchestration abilities remain absent. Business systems comprise modules like portals, pages, components, models, and services—their organizational relationships determine architecture quality. Traditional tools lack visual orchestration support, hiding system structures within code and making maintenance difficult. Software development is undergoing a shift from programming-centric to balanced programming and orchestration, where orchestration-oriented architectures, frameworks, and tools deliver both improved development efficiency and sustained architectural elegance.

Traditional low-code/visual development platforms rely on black-box rule engines, fundamentally limiting application extensibility. They sacrifice expressive power for simplicity, inevitably failing in complex enterprise scenarios. True visual development shouldn't constrain capabilities—it should enable developers to orchestrate system modules and technical capabilities visually, transitioning from closed DSL (Domain Specific Language) engines to open orchestration protocols, from limited expression to unlimited integration.

While AI technologies, particularly large language models, have seen explosive growth in recent years, their enterprise-scale adoption has fallen far short of expectations. Countless AI projects remain stuck in the demo phase, failing to deliver meaningful business value. The gap between promise and reality remains vast.

Unlike traditional enterprise web applications, production-grade AI applications carry inherent complexity. They're fundamentally about executing tasks, not merely recording transactions. This requires deep integration with each organization's unique business processes, data ecosystems, and knowledge systems. Custom development becomes inevitable, yet the reality is prohibitively expensive and often disappointing. Traditional development paradigms are no longer sufficient—the market urgently needs AI-native engineering practices and development methodologies.

AI-native application architecture must address not only how AI modules are designed and integrated, but also how traditional technical modules are perceived, driven, and orchestrated by AI. Attempting to integrate AI capabilities into legacy event-driven architectures is like putting an internal combustion engine on a horse cart—foolish and inefficient. Traditional enterprise applications like ERP, CRM, and OA systems will inevitably be reshaped by new AI-native architectures and development paradigms.