1ANTHATI KARTHIK, 2A. MANDHAN CHARI 03 07 The rapid advancement of Large Language Models LLMs has significantly transformed artificial intelligence by enabling machines to perform complex natural language understanding reasoning content generation and decisionmaking tasks Despite their remarkable performance existing LLMbased agents primarily rely on static pretrained knowledge making them incapable of continuously adapting to newly emerging information and userspecific experiences This limitation often results in outdated knowledge limited personalization contextual inconsistency and catastrophic forgetting when models are retrained To overcome these challenges this research proposes a comprehensive roadmap for Lifelong Learning of Large Language Model Based Agents integrating continuous knowledge acquisition persistent memory adaptive reasoning RetrievalAugmented Generation RAG vectorbased semantic memory knowledge graphs explainable artificial intelligence XAI and feedbackdriven learning mechanisms The proposed framework enables intelligent agents to acquire knowledge incrementally from user interactions external repositories and dynamic information sources while preserving previously learned knowledge through efficient memory consolidation techniques A semantic vector database stores contextual representations that facilitate fast retrieval and contextaware reasoning during future interactions Furthermore knowledge graph integration enhances structured knowledge representation and semantic relationship discovery thereby improving inference capability and decisionmaking accuracy Explainable AI modules increase system transparency by providing interpretable reasoning pathways while continuous feedback learning refines knowledge quality and response generation through iterative optimization An analytics dashboard continuously monitors memory growth retrieval efficiency reasoning performance and learning progression enabling proactive system evaluation and improvement Experimental evaluation demonstrates that the proposed lifelong learning framework significantly improves contextual continuity factual consistency adaptive learning capability response relevance and user personalization compared with conventional LLM architectures Moreover the framework effectively reduces hallucination enhances knowledge retention minimizes redundant retraining and supports scalable deployment across diverse realworld applications including education healthcare enterprise knowledge management scientific research software engineering and intelligent virtual assistants The proposed roadmap establishes a robust foundation for developing nextgeneration autonomous AI agents capable of continuous learning longterm knowledge evolution and intelligent decisionmaking in dynamic environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026