This research proposes a holistic agentic Artificial Intelligence framework that seeks to solve the palpable requirement of autonomy, versatility, and world-scale generative Artificial Intelligence systems. When framing the concept of an agentic Artificial Intelligence as a paradigm shift where Large Language Models are relatively reactive, tool-augmented to a proactive, modular agent that pursues goals in the long term, the research discusses how large language models are becoming an architectural mandate. The proposed research uses a skillful literature synthesis and develops a concept and technical framework that integrates perception, memory, planning, execution, and communication modules. In contrast to current frameworks like AutoGPT and ReAct, where the deep memory, explainability, and certain control of scalability are often lacking, the proposed framework offers a solution with persistent memory layers, semantic routing, and modular orchestration pipelines when it comes to cloud-native deployments. Experimental verification offers a greater degree of autonomy, coordination, and resilience in a diverse range of activities such as enterprise automation and robotics. It is also an edge deployment with the help of lightweight microservices framework. In practice, this method supports scaled, comprehensible agents adapted to long-loop thinking and human-in-the-loop management and adaptive value chain serving. The novelty of the work is attributed to its reusable architecture, as it is not only capable of modifying agentic behavior alone, but it can also connect the theoretical principles and industry feasibility. Future efforts will be with the integration of ethical governance, uniform benchmarking, and multimodal memory remodeling in next generation of real-world autonomous systems.
Alva et al. (2026) studied this question.