Self-Evolving Multi-Agent Systems for Autonomous Task Planning and Decision Intelligence
Abstract
The increasing complexity of enterprise workflows, intelligent automation, and dynamic decision-making environments has accelerated the development of autonomous artificial intelligence systems capable of performing complex tasks with minimal human intervention. Traditional single-agent AI architectures often face limitations in scalability, adaptability, collaboration, and real-time decision-making when operating in uncertain and continuously evolving environments. This research proposes a Self-Evolving Multi-Agent System (SEMAS) framework that enables autonomous task planning and intelligent decision-making through collaborative interactions among specialized AI agents. The proposed architecture integrates large language models (LLMs), reinforcement learning, knowledge graphs, dynamic memory, and adaptive planning mechanisms to allow agents to perceive, reason, communicate, and continuously improve their capabilities based on environmental feedback. Each autonomous agent is assigned specialized responsibilities, including task decomposition, planning, execution, monitoring, validation, and optimization, while a centralized orchestration layer coordinates inter-agent collaboration and resource allocation. The framework incorporates self-evolution mechanisms such as experience replay, continual learning, policy refinement, and performance-driven adaptation to enhance long-term system intelligence without requiring complete model retraining. Experimental evaluation demonstrates that the proposed SEMAS framework significantly improves task completion accuracy, planning efficiency, decision quality, adaptability, and resource utilization compared with conventional single-agent and static multi-agent systems. Furthermore, the architecture exhibits strong robustness under dynamic environments, uncertain task conditions, and heterogeneous computational resources. The proposed framework provides a scalable and trustworthy solution for intelligent automation across domains including enterprise workflow management, healthcare, cybersecurity, robotics, smart manufacturing, autonomous transportation, scientific research, and digital governance. The findings establish self-evolving multi-agent systems as a promising paradigm for next-generation autonomous artificial intelligence capable of collaborative reasoning, adaptive learning, and intelligent decision support.
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