This research explores the design of Large Language Model (LLM) teams by applying the formal principles of distributed computing. The authors argue that multi-agent systems face the same challenges as networked computers, including communication overhead, consistency conflicts, and task synchronization. By using Amdahl’s Law, the study demonstrates that a team's efficiency is strictly limited by how much of a task can be performed in parallel. Experimental results show that while decentralized teams are more resilient to individual "straggler" agents, they often suffer from higher token costs and coordination errors. Ultimately, the paper provides a principled framework to move beyond trial-and-error when building robust and cost-effective AI collectives.
linktree: https://linktr.ee/learnbydoingwithstevenarvix: https://arxiv.org/pdf/2603.12229
