# Toward Agentic Environments: GenAI and the Convergence of AI, Sustainability, and Human-Centric Spaces arXiv: https://arxiv.org/abs/2512.15787 ยท HTML: https://mlsanity.com/paper/2512.15787/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 34 of its 86 references that are in the map. - First posted on arXiv 15 Dec 2025. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 30 of 34) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024) - [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021) - [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024) - [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022) - [Sources of Hallucination by Large Language Models on Inference Tasks](https://mlsanity.com/paper/2305.14552/) (2023) - [The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey](https://mlsanity.com/paper/2404.11584/) (2024) - [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024) - [A Review on Edge Large Language Models: Design, Execution, and Applications](https://mlsanity.com/paper/2410.11845/) (2024) - [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022) - [Large Language Models Empowered Autonomous Edge AI for Connected Intelligence](https://mlsanity.com/paper/2307.02779/) (2023) - [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024) - [How Does Quantization Affect Multilingual LLMs?](https://mlsanity.com/paper/2407.03211/) (2024) - [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024) - [Prithvi WxC: Foundation Model for Weather and Climate](https://mlsanity.com/paper/2409.13598/) (2024) - [Apple Intelligence Foundation Language Models: Tech Report 2025](https://mlsanity.com/paper/2507.13575/) (2025) - [From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion](https://mlsanity.com/paper/2308.02560/) (2023) - [Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward](https://mlsanity.com/paper/2402.01799/) (2024) - [Edge AI: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2407.04053/) (2024) - [Data Management For Training Large Language Models: A Survey](https://mlsanity.com/paper/2312.01700/) (2023) - [The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans](https://mlsanity.com/paper/2303.06219/) (2023) - [Super Tiny Language Models](https://mlsanity.com/paper/2405.14159/) (2024) - [Generative AI-Enhanced Multi-Modal Semantic Communication in Internet of Vehicles: System Design and Methodologies](https://mlsanity.com/paper/2409.15642/) (2024) - [Data Cleaning Using Large Language Models](https://mlsanity.com/paper/2410.15547/) (2024) ## Abstract (arXiv) 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.