The text discusses multi-agent systems, which are becoming increasingly prevalent due to the need for scalability and modularity in complex domains. The authors explore different architectures, including generic vs custom ones, and benchmark their performance using a modified version of the Tau-bench dataset. They compare three architectures: single agent, swarm, and supervisor, and find that the supervisor architecture outperforms the others when dealing with multiple distractor environments. The authors also discuss improvements made to the supervisor architecture, which resulted in a nearly 50% increase in performance on the benchmark. The study highlights the importance of considering scalability and modularity in multi-agent systems and provides insights into how generic architectures can be used effectively.