This publication presents the design and implementation of a multi-agent AI system in which multiple autonomous agents collaborate to complete complex tasks. The system demonstrates agent coordination, role-based execution, and decision-making using large language models within an agentic architecture.
The system follows a modular multi-agent architecture where each agent is assigned a specific role such as planning, reasoning, execution, or validation. A central coordinator manages task distribution and agent communication. Large Language Models are used to enable intelligent decision-making and autonomous behavior across agents.
The implemented multi-agent system successfully demonstrated effective coordination between agents, improved task execution efficiency, and scalability compared to single-agent approaches. The architecture is suitable for real-world agentic AI applications such as automation, cybersecurity, and intelligent assistants.