World Models for Autonomous AI Agents: A Comprehensive Review

Mrs. Deepali Jayprakash Trigule1

1Computer Technology Department,S.E.S.Polytechnic,Solapur

Abstract - Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their limitations in reasoning, planning, long-term memory retention, and interaction with dynamic environments have motivated the development of World Models. World Models enable intelligent agents to construct internal representations of their surroundings, predict future states, perform reasoning, and make autonomous decisions. This review provides a comprehensive analysis of World Model architectures, memory integration mechanisms, evaluation methodologies, explainability techniques, and real-world deployment strategies. Existing research trends, challenges, and open issues are discussed, highlighting future research directions for developing reliable and autonomous AI systems.

Key Words:  World Models, Agentic AI, Large Language Models, Autonomous Agents, Memory Systems, Explainable AI, Evaluation Metrics, Digital Twins, Reinforcement Learning.