Agentic Hybrid Quantum–Neuromorphic AI Architecture With Explainable Reinforcement Learning for Next-Generation Intelligent Systems

Artificial intelligence systems are shifting away to passive prediction systems into architectures that are supposed to rationalize and modify according to their needs as well as explain what they are doing. The current methods tend to maximize the precision at the expense of transparency and long-term flexibility, creating a gap in the systems with both accountable reasoning and learning processes. To solve this, we present an Agentic Hybrid Quantum -Neuromorphic AI system with an Explainable Reinforcement Learning core. It consists of a combination of quantum-assisted exploration, spiking neuromorphic adaptation, interpretable policy optimization to enable the system to adapt computational strategy depending on the dynamics of rewards, confidence, and environmental change. Experiments exhibit enhanced convergence speed, ability to maintain behavior in the case of sudden changes in the environment and a far greater enhancement in explanation coherence than traditional reinforcement learning agents and adaptive immersive systems.