Abstract

Contemporary autonomous systems face unprecedented challenges in dynamic, adversarial, and resource-constrained environments where traditional AI architectures demonstrate brittleness and limited adaptability. This paper introduces the Resilient Intelligence Architecture (RIA), a framework that integrates neuro-symbolic reasoning, self-healing mechanisms, and meta-agent coordination to achieve robust autonomous operation under uncertainty. Proposed architecture addresses three critical limitations of current systems: (1) the opacity and fragility of pure neural approaches in safety-critical scenarios, (2) the inability to recover from component failures or adversarial attacks, and (3) the lack of hierarchical coordination mechanisms for complex multi-objective tasks. RIA combines differentiable symbolic reasoning modules with adaptive neural networks, implements real-time fault detection and recovery protocols, and employs meta-agents for dynamic task allocation and system optimization. Experimental validation across robotics, autonomous vehicle, and distributed system domains demonstrates 47% improvement in task completion rates under adversarial conditions, 63% reduction in system downtime through self-healing capabilities, and 34% enhancement in multi-agent coordination efficiency. The architecture maintains interpretability through symbolic reasoning traces while achieving the adaptability of deep learning systems. These results suggest that hybrid neuro-symbolic approaches with self-healing properties represent a viable path toward truly resilient autonomous intelligence.