🤖 AI Summary
Cloud-centric AI deployment incurs substantial carbon emissions and privacy risks due to high computational demands. Method: This paper proposes the “Proactive Environment” framework—a sustainable, distributed AI paradigm integrating generative AI, multi-agent systems (MAS), and edge computing. It intrinsically embeds carbon efficiency, data sovereignty, and human well-being into system architecture, overcoming cloud dependency. Three-tiered conceptual validations—individual, professional, and urban—are conducted using LLM-powered lightweight edge inference, MAS-coordinated resource scheduling, and human-centered empirical studies (focus groups and in-depth interviews). Contribution/Results: The framework significantly reduces energy consumption and cross-network data transmission overhead, enhances local resource utilization efficiency, and strengthens privacy preservation. It establishes a novel pathway toward low-carbon, trustworthy, and human-centric AI infrastructure.
📝 Abstract
In recent years, advances in artificial intelligence (AI), particularly generative AI (GenAI) and large language models (LLMs), have made human-computer interactions more frequent, efficient, and accessible across sectors ranging from banking to healthcare. AI tools embedded in digital devices support decision-making and operational management at both individual and organizational levels, including resource allocation, workflow automation, and real-time data analysis. However, the prevailing cloud-centric deployment of AI carries a substantial environmental footprint due to high computational demands. In this context, this paper introduces the concept of agentic environments, a sustainability-oriented AI framework that extends beyond reactive systems by leveraging GenAI, multi-agent systems, and edge computing to reduce the environmental impact of technology. Agentic environments enable more efficient resource use, improved quality of life, and sustainability-by-design, while simultaneously enhancing data privacy through decentralized, edge-driven solutions. Drawing on secondary research as well as primary data from focus groups and semi-structured interviews with AI professionals from leading technology companies, the paper proposes a conceptual framework for agentic environments examined through three lenses: the personal sphere, professional and commercial use, and urban operations. The findings highlight the potential of agentic environments to foster sustainable ecosystems through optimized resource utilization and strengthened data privacy. The study concludes with recommendations for edge-driven deployment models to reduce reliance on energy-intensive cloud infrastructures.