AI-native Applications: Study Defines Characteristics and Quality Attributes of Emerging Software Paradigm

The emergence of artificial intelligence is driving a new era in software development, giving rise to AI-native applications that challenge traditional engineering approaches. Lingli Cao, Shanshan Li, and Ying Fan from Nanjing University, alongside Danyang Li and Chenxing Zhong from Nanjing University of Science and Technology, investigate this rapidly evolving landscape by systematically analysing existing knowledge from both academic and practical sources. Their work addresses a critical gap in understanding, establishing a clear definition and architectural blueprint for these innovative systems. The team’s research reveals that AI-native applications fundamentally differ from conventional software, distinguished by their reliance on artificial intelligence as a core intelligence paradigm and their inherent probabilistic nature, demanding new quality attributes such as AI-specific observability and a focus on outcome predictability. This comprehensive analysis provides essential guidance for developers and researchers seeking to build and evaluate the next generation of software applications.

AI-Native Applications And Quality Assurance Needs

This research details a comprehensive exploration of the emerging field of AI-native applications, revealing that these applications represent a significant evolution in software engineering, characterized by intelligent automation, continuous learning, and multimodal capabilities. This shift fundamentally changes the focus of software quality, moving away from deterministic code and towards managing the complexities of probabilistic systems, necessitating a new approach to quality assurance that emphasizes observability, reliability, and economic efficiency. The research highlights that AI-native applications are distinguished by their reliance on artificial intelligence as the core intelligence paradigm and their inherent probabilistic, non-deterministic nature. This necessitates a re-evaluation of traditional quality metrics, prioritizing observability, reliability, and cost-effectiveness.

Realizing the full potential of AI-native applications is currently constrained by integration challenges and stability concerns, emphasizing the importance of monitoring agentic workflows, RAG pipelines, and model outputs in real-time. A transition from cloud-native to AI-native architectures is required, adapting orchestration layers and service meshes to accommodate probabilistic services. Long-term maintenance presents a significant challenge, demanding methods for detecting concept drift, managing dependencies, and evaluating economic viability. The research employed a thorough review of grey literature, including reports, white papers, blog posts, and conference proceedings, alongside formal academic publications, using a specific set of criteria to assess source quality and reliability, and employing thematic synthesis to map the AI-native application landscape.

AI Native Apps, Grey Literature Review Protocol

This study pioneers a systematic understanding of AI-native applications through a comprehensive grey literature review, integrating insights from industry perspectives and practical implementations. Researchers conducted targeted searches on Google and Bing, focusing on industry reports, technical blogs, and leading open-source projects hosted on GitHub, guided by a structured protocol ensuring rigorous source selection, consistent quality assessment, and thorough thematic analysis. The research team identified 106 relevant studies based on predefined selection criteria, carefully evaluating each source for its contribution to understanding the emerging paradigm. Thematic analysis revealed that AI-native applications are fundamentally distinguished by two core pillars: the central role of artificial intelligence as the primary intelligence paradigm and their inherent probabilistic, non-deterministic nature. Further investigation pinpointed critical quality attributes essential for successful AI-native applications, including reliability, usability, performance efficiency, and AI-specific observability, alongside a typical technology stack comprising LLM orchestration frameworks, vector databases, and AI-native observability platforms, prioritizing response quality, cost-effectiveness, and outcome predictability.

AI-Native Apps Defined By Core Attributes

This research delivers the first comprehensive understanding of AI-native applications, establishing a foundation for their systematic design and development. The team identified and analyzed 106 studies, integrating insights from industry reports, technical blogs, and open-source projects to define the core characteristics of this emerging software paradigm. Results demonstrate that AI-native applications are fundamentally distinguished by two pillars: the central role of artificial intelligence as the system’s intelligence paradigm and their inherent probabilistic, non-deterministic nature. The study meticulously synthesized critical quality attributes essential for these applications, including reliability, usability, performance efficiency, and AI-specific observability, revealing the unique challenges associated with ensuring these attributes in systems driven by probabilistic AI models. Furthermore, the research mapped the prevailing technology stacks supporting AI-native applications, identifying a common pattern comprising large language model orchestration frameworks, vector databases, and AI-native observability platforms. Measurements confirm that these systems prioritize response quality, cost-effectiveness, and outcome predictability, culminating in the proposal of a novel dual-layered engineering blueprint for AI-native applications, providing actionable design guidelines and technical recommendations for practitioners.

AI-Native Applications, Blueprint and Quality Attributes

This study establishes a foundational understanding of AI-native applications, identifying core characteristics that distinguish them from conventional software systems. Researchers determined that these applications are fundamentally defined by the central role of artificial intelligence as the primary intelligence paradigm and their inherent probabilistic, non-deterministic nature, representing the first attempt to propose a dual-layered engineering blueprint for designing and building these systems, offering actionable guidelines and technical recommendations for practitioners. The analysis reveals that reliability, usability, performance efficiency, and AI-specific observability are critical quality attributes for AI-native applications. Furthermore, a typical technology stack is emerging, comprising LLM orchestration frameworks, vector databases, and AI-native observability platforms, all focused on achieving high response quality, cost-effectiveness, and predictable outcomes. While this research provides a significant step forward, the authors acknowledge the need for further investigation into the long-term implications of these architectural patterns and the evolving landscape of AI technologies.