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

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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.