Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
- URL: http://arxiv.org/abs/2508.15411v2
- Date: Fri, 19 Sep 2025 11:28:06 GMT
- Title: Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
- Authors: Frederik Vandeputte,
- Abstract summary: We argue that future GenAI-native systems should integrate GenAI's cognitive capabilities with software engineering principles to create robust, adaptive, and efficient systems.<n>We introduce foundational GenAI-native design principles centered around five key pillars -- reliability, excellence, evolvability, self-reliance, and assurance.<n>We outline the key ingredients of a GenAI-native software stack and discuss the impact of these systems from technical, user adoption, economic, and legal perspectives.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative AI (GenAI) has emerged as a transformative technology, demonstrating remarkable capabilities across diverse application domains. However, GenAI faces several major challenges in developing reliable and efficient GenAI-empowered systems due to its unpredictability and inefficiency. This paper advocates for a paradigm shift: future GenAI-native systems should integrate GenAI's cognitive capabilities with traditional software engineering principles to create robust, adaptive, and efficient systems. We introduce foundational GenAI-native design principles centered around five key pillars -- reliability, excellence, evolvability, self-reliance, and assurance -- and propose architectural patterns such as GenAI-native cells, organic substrates, and programmable routers to guide the creation of resilient and self-evolving systems. Additionally, we outline the key ingredients of a GenAI-native software stack and discuss the impact of these systems from technical, user adoption, economic, and legal perspectives, underscoring the need for further validation and experimentation. Our work aims to inspire future research and encourage relevant communities to implement and refine this conceptual framework.
Related papers
- Charting Uncertain Waters: A Socio-Technical Framework for Navigating GenAI's Impact on Open Source Communities [53.812795099349295]
We conduct a scenario-driven, conceptual exploration using a socio-technical framework inspired by McLuhan's Tetrad to surface both risks and opportunities for community resilience amid GenAI-driven disruption of OSS development across four domains: software practices, documentation, community engagement, and governance.<n>By adopting this lens, OSS leaders and researchers can proactively shape the future of their ecosystems, rather than simply reacting to technological upheaval.
arXiv Detail & Related papers (2025-08-06T22:54:15Z) - Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
arXiv Detail & Related papers (2025-05-13T17:59:20Z) - Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption: Bridging Innovation and Governance in Large Organisations [55.2480439325792]
Generative Artificial Intelligence is a powerful new technology with the potential to boost innovation and reshape governance in many industries.<n>However, organisations face major challenges in scaling GenAI, including technology complexity, governance gaps and resource misalignments.<n>This study explores how Enterprise Architecture Management can meet the complex requirements of GenAI adoption within large enterprises.
arXiv Detail & Related papers (2025-05-09T07:41:33Z) - Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research [1.6311895940869516]
We consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts.<n>We explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism.
arXiv Detail & Related papers (2025-02-25T16:34:23Z) - GenAIOps for GenAI Model-Agility [2.7396907658239424]
We discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions.<n>First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models.<n>We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools.
arXiv Detail & Related papers (2024-12-19T03:29:03Z) - Generating a Low-code Complete Workflow via Task Decomposition and RAG [0.040964539027092926]
GenAI-based systems are more difficult to design due to their scale and versatility.<n>We formalize two techniques, Task Decomposition and Retrieval-Augmented Generation, as design patterns for GenAI-based systems.<n>As these two patterns affect the entire AI development cycle, we explain how they impacted the dataset creation, model training, model evaluation, and deployment phases.
arXiv Detail & Related papers (2024-11-29T20:13:56Z) - The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles [65.14115295214636]
We specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers.
We introduce various GenAI techniques used in each layer of IoEV applications.
Public datasets available for training the GenAI models are summarized.
arXiv Detail & Related papers (2024-09-24T05:12:10Z) - Generative AI and Process Systems Engineering: The Next Frontier [0.5937280131734116]
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE)
These cutting-edge GenAI models, particularly foundation models (FMs), are pre-trained on extensive, general-purpose datasets.
The article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety.
arXiv Detail & Related papers (2024-02-15T18:20:42Z) - From Generative AI to Generative Internet of Things: Fundamentals,
Framework, and Outlooks [82.964958051535]
Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making.
By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society.
arXiv Detail & Related papers (2023-10-27T02:58:11Z) - AutoML in The Wild: Obstacles, Workarounds, and Expectations [37.813441975457735]
This study focuses on understanding the limitations of AutoML encountered by users in their real-world practices.
Our findings reveal that users actively exercise user agency to overcome three major challenges arising from customizability, transparency, and privacy.
arXiv Detail & Related papers (2023-02-21T17:06:46Z) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.