Unlocking AI Creativity: A Multi-Agent Approach with CrewAI - Venkadesh, P.; Divya, S. V.; Kumar, K. Subash (Journal of Trends in Computer Science and Smart Technology, 2024)
By automating processes that traditionally require human intelligence, such as learning, reasoning, and problem-solving, Artificial Intelligence (AI) has transformed many industries. Even with these developments, System 1 thinking is quick, providing instinctive reactions, while System 2 thinking entails thorough analysis and reasoned decision-making. AI systems still have a long way to go before they can replicate System 2 thinking—deliberate, analytical, and essential for managing complex problems. This study investigates how Multi-Agent Systems (MAS) integrate System 2 thinking into AI, concentrating on CrewAI, a no-code framework designed to improve AI creativity and decision-making. Unlike TensorFlow Agents and OpenAI’s Gym, which are limited to single-agent reinforcement learning, CrewAI excels in handling multi-agent, real-world tasks through collaboration. This study explores practical applications of CrewAI, such as intelligent grid management, automated customer support, and advertising. These examples highlight how CrewAI promotes AI creativity and problem-solving through cooperative agent interactions, leveraging System 2 thinking. Problems like scalability and coordination are also addressed, with solutions such as dynamic role assignment and hierarchical task management. In summary, the integration of System 2 thinking into MAS frameworks like CrewAI signifies progress toward creating intelligent, dependable AI systems capable of tackling the complexities of real-world problems.
Keywords: System Thinking; Multi-Agent System; CrewAI; AI Creativity; Decision-Making