UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces
Abstract
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.
Cite
@article{arxiv.2505.00472,
title = {UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces},
author = {Alaa Saleh and Sasu Tarkoma and Praveen Kumar Donta and Anders Lindgren and Naser Hossein Motlagh and Schahram Dustdar and Susanna Pirttikangas and Lauri Lovén},
journal= {arXiv preprint arXiv:2505.00472},
year = {2026}
}