HARSA: A Hybrid Autonomous Reasoning and Safety-Aware Agent Architecture to Support Scalable and Reliable Intelligent Decision-Making

Abstract

Intelligent agents can be developed with the help of the fast progress of autonomous artificial intelligence systems, which allows them to act in complex and dynamic environments. However, the existing practices tend to be constrained due to the lack of safety awareness, the inappropriateness of contextual reasoning, and susceptibility to the instability in the learning process. This paper focuses on overcoming these challenges by proposing a new model, HARSA (Hybrid Autonomous Reasoning and Safety-Aware Agent), a combination of reinforcement learning, large language model-based reasoning, memory-augmented state representation, safety-constrained optimization, and continual learning within one model. Experimental assessment is significant improvement regarding numerous performance metrics. Cumulative reward will increase to 740 and the policy loss will decrease to 0.15 indicating an effective and stable convergence. The model also has high decision accuracy of 94% and safety compliance of 96% which is better than the traditional reinforcement learning, deep reinforcement learning, LLM-based agents, and hybrid AI systems. The results corroborate the above argument that HARSA is a viable means of eliminating the performance versus safety trade-off providing a scalable, robust, and reliable solution to real-life applications such as healthcare, smart systems, and autonomous decision-making.