A Unified Agentic AI Framework Integrating Memory, Learning, Reasoning, and Autonomous Action Execution

Abstract

Large Language Models (LLMs) have made significant strides in the field of Artificial Intelligence (AI) for various applications such as natural language understanding, knowledge generation, and human-AI interaction. But current LLM-based systems fail to be fully autonomous because of poor memory retention, limited adaptive learning ability, unreliable long-term reasoning, and human-driven instructions. This research introduces a Unified Agentic AI Framework (U-AIAF) which combines the features of memory, learning, reasoning, and autonomous action execution within a single closed-loop intelligent architecture. The proposed framework learned persistent knowledge by adjusting the dynamic memory management, improved decision-making by reasoning-based planning, adapted behaviour by feedback-driven learning, and autonomous action by continuously interacting with external environments. A benchmark evaluation framework of 1000 independent tasks was created to evaluate the proposed architecture against information retrieval, decision making, multi-step planning, tool execution and adaptive learning scenarios. The proposed framework was evaluated using the following measures: task completion accuracy, reasoning success rate, execution reliability, and computational efficiency, when compared with conventional LLM-based agents and memory-augmented agents. The results showed that the proposed framework successfully completed the tasks with an accuracy of 94.8%, succeeded in reasoning with a rate of 93.5%, and reached an accuracy of 95.2% in execution when compared to approaches used as baseline. Ablation analysis also revealed that each of the memories, learning, reasoning, and feedback mechanisms had a unique contribution to the enhancement of autonomous performance. The proposed framework is a comprehensive system that provides a framework for the development of next-generation agentic AI systems capable of continuous learning, intelligent reasoning, and autonomous task execution. The architecture presented here offers great potential in areas such as intelligent assistants, autonomous decision-support systems, robotics and adaptive digital platforms.