This blog explores different multi-agent architectures, focusing on their motivations, constraints, and performance improvements, particularly in the context of a modified Tau-bench dataset. It examines the reasons for adopting multi-agent systems, such as scalability, modularity, and the ability to integrate contributions from diverse developers. The text discusses specific architectures, including single agent, swarm, and supervisor models, highlighting the strengths and challenges of each, with an emphasis on the supervisor architecture's need for improvements to enhance performance. Experiments revealed that while the single agent model struggles with multiple distractor domains, the swarm and supervisor architectures, despite their complications in communication and translation, offer better scalability and efficiency. The text concludes with a discussion on the future of multi-agent systems, suggesting that generic architectures, particularly the supervisor model with enhancements, may become more prevalent due to their ease of development and scalability across multiple domains.