Authors : Vivekram Kantheti; V. V. S. Vinay Kankatala; Baby Saranya Nallanmeli; Mohan Sri Sai Panduri; Tirupati Rao Edupalli; Sneha Pradhan; Jahnavi Velli

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/3dradw56

Scribd : https://tinyurl.com/ya6k843t

DOI : https://doi.org/10.38124/ijisrt/26mar1260

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid advancement of Generative Artificial Intelligence has highlighted the limitations of single-agent large language model (LLM) systems in handling complex, multi-step problem-solving tasks . Multi-Agent Systems (MAS) address these limitations by enabling multiple autonomous agents to collaborate, each with specialized roles and responsibilities. This paper presents a structured approach to designing and implementing a Multi-Agent System using the CrewAI framework. Crew AI facilitates role-based agent orchestration, task delegation, and inter-agent coordination, enabling efficient decomposition of complex objectives into manageable subtasks. The proposed system architecture defines agents with distinct goals, backstories, and tool access, coordinated through a centralized crew mechanism that ensures coherent task execution and output integration. A practical implementation scenario is discussed to demonstrate how Crew AI-based multi-agent collaboration improves reasoning accuracy, scalability, and modularity compared to traditional single-agent architectures. The results indicate that Crew AI-driven MAS enhances productivity, maintainability, and adaptability, making it suitable for real-world applications such as intelligent tutoring systems, research assistants, and decision-support platforms. This study emphasizes the potential of Crew AI as an effective framework for building scalable and collaborative GenAI systems.

Keywords : Multi-Agent Systems, Crew AI Framework, Generative Artificial Intelligence, Autonomous AI Agents, Agent-Oriented Architecture, Task Orchestration, Intelligent Mentoring Systems, LLM-Based Agents, Collaborative AI Systems, Scalable AI Architectures.

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