UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces

TL;DR

UserCentrix is a hybrid agentic framework for smart spaces that improves resource management and user experience by dynamically balancing latency, accuracy, and computational costs through intent-driven decision-making.

Contribution

It introduces a novel agentic orchestration framework that integrates autonomous decision-making modules for resource optimization in smart environments.

Findings

Efficient intent processing and real-time monitoring achieved.

Framework balances reasoning quality and computational efficiency.

Effective under resource-constrained edge conditions.

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.