UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911570565529600 |
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| author | Saleh, Alaa Tarkoma, Sasu Donta, Praveen Kumar Lindgren, Anders Motlagh, Naser Hossein Dustdar, Schahram Pirttikangas, Susanna Lovén, Lauri |
| author_facet | Saleh, Alaa Tarkoma, Sasu Donta, Praveen Kumar Lindgren, Anders Motlagh, Naser Hossein Dustdar, Schahram Pirttikangas, Susanna Lovén, Lauri |
| contents | Agentic Artificial Intelligence (AI) constitutes a transformative paradigm in the evolution of intelligent agents and decision-support systems, redefining smart environments by enhancing operational efficiency, optimizing resource allocation, and strengthening systemic resilience. This paper presents UserCentrix, a hybrid agentic orchestration framework for smart spaces that optimizes resource management and enhances user experience through urgency-aware and intent-driven decision-making mechanisms. The framework integrates interactive modules equipped with agentic behavior and autonomous decision-making capabilities to dynamically balance latency, accuracy, and computational cost. User intent functions as a governing control signal that prioritizes decisions, regulates task execution and resource allocation, and guides the adaptation of decision-making strategies to balance trade-offs between speed and accuracy. Experimental results demonstrate that the framework autonomously enables efficient intent processing and real-time monitoring, while balancing reasoning quality and computational efficiency, particularly under resource-constrained edge conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces Saleh, Alaa Tarkoma, Sasu Donta, Praveen Kumar Lindgren, Anders Motlagh, Naser Hossein Dustdar, Schahram Pirttikangas, Susanna Lovén, Lauri Artificial Intelligence Distributed, Parallel, and Cluster Computing Multiagent Systems Networking and Internet Architecture Agentic Artificial Intelligence (AI) constitutes a transformative paradigm in the evolution of intelligent agents and decision-support systems, redefining smart environments by enhancing operational efficiency, optimizing resource allocation, and strengthening systemic resilience. This paper presents UserCentrix, a hybrid agentic orchestration framework for smart spaces that optimizes resource management and enhances user experience through urgency-aware and intent-driven decision-making mechanisms. The framework integrates interactive modules equipped with agentic behavior and autonomous decision-making capabilities to dynamically balance latency, accuracy, and computational cost. User intent functions as a governing control signal that prioritizes decisions, regulates task execution and resource allocation, and guides the adaptation of decision-making strategies to balance trade-offs between speed and accuracy. Experimental results demonstrate that the framework autonomously enables efficient intent processing and real-time monitoring, while balancing reasoning quality and computational efficiency, particularly under resource-constrained edge conditions. |
| title | UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing Multiagent Systems Networking and Internet Architecture |
| url | https://arxiv.org/abs/2505.00472 |