Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications

2025-09-16

AI Summary

AI-native applications lack a unified engineering definition and architectural guidance, hindering systematic design and quality assurance. Method: We propose a dual-pillar model—“AI intelligence paradigm + probabilistic systems”—and establish, for the first time, a two-layer engineering framework for next-generation software. Through gray literature review and empirical analysis of 106 high-quality GitHub open-source projects, augmented by thematic modeling and a rigorous quality assessment protocol, we identify four critical quality attributes: reliability, availability, performance efficiency, and AI observability. Contribution/Results: We distill a canonical technology stack comprising LLM orchestration frameworks, vector databases, and AI-native monitoring platforms. The framework delivers a systematic architectural blueprint, principled design guidelines, and evidence-based technology selection criteria for AI-native applications—advancing both research and industrial practice in AI engineering.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Intelligent User Interfaces

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Background: The rapid advancement of large language models (LLMs) has given rise to AI-native applications, a new paradigm in software engineering that fundamentally redefines how software is designed, developed, and evolved. Despite their growing prominence, AI-native applications still lack a unified engineering definition and architectural blueprint, leaving practitioners without systematic guidance for system design, quality assurance, and technology selection. Objective: This study seeks to establish a comprehensive understanding of AI-native applications by identifying their defining characteristics, key quality attributes, and typical technology stacks, as well as by clarifying the opportunities and challenges they present. Method: We conducted a grey literature review, integrating conceptual perspectives retrieved from targeted Google and Bing searches with practical insights derived from leading open-source projects on GitHub. A structured protocol encompassing source selection, quality assessment, and thematic analysis was applied to synthesize findings across heterogeneous sources. Results: We finally identified 106 studies based on the selection criteria. The analysis reveals that AI-native applications are distinguished by two core pillars: the central role of AI as the system's intelligence paradigm and their inherently probabilistic, non-deterministic nature. Critical quality attributes include reliability, usability, performance efficiency, and AI-specific observability. In addition, a typical technology stack has begun to emerge, comprising LLM orchestration frameworks, vector databases, and AI-native observability platforms. These systems emphasize response quality, cost-effectiveness, and outcome predictability, setting them apart from conventional software systems. Conclusion: This study is the first to propose a dual-layered engineering blueprint...

Problem

• Defining AI-native applications' engineering characteristics and architecture
• Identifying key quality attributes for AI-native software systems
• Establishing typical technology stacks for AI-native application development

Innovation

• LLM orchestration frameworks for system intelligence
• Vector databases for probabilistic data handling
• AI-native observability platforms for quality