Problem

Despite the increasing capabilities and widespread deployment of large language model (LLM) teams, there is a significant lack of a principled framework for understanding their behavior. Key questions such as when LLM teams are beneficial, the optimal number of agents, and how team structure impacts performance remain largely unanswered. Current design and deployment strategies often rely on trial-and-error, leading to substantial inefficiencies, wasted computational resources, and potential failures like agents overwriting each other, producing redundant outputs, conflicting in decisions, propagating errors, and reinforcing incorrect conclusions. The inherent limitations of single LLMs in terms of memory, context, and reliability necessitate teaming, but without a formal foundation, these multi-agent systems risk being inefficient, unreliable, and costly.

Method

The authors propose utilizing distributed systems as a principled foundation for the design and evaluation of LLM teams. They establish a formal correspondence between LLM teams and distributed systems based on four core properties: independence of agents/nodes, communication for coordination, concurrency in task execution, and fallibility of individual components. To empirically test this analogy, two experiments were conducted using teams of 1 to 5 homogeneous LLM agents (Claude-Sonnet-4-6, Gemini 3-Flash, or GPT-5.2). Agents were tasked with collaborative coding problems in three domains: a math utilities library, simulated data analysis, and SVG rendering. The experiments manipulated two main factors: task parallelizability (highly parallel, mixed, or highly serial dependencies between subtasks) and team architecture (Experiment 1: centralized with pre-assigned tasks; Experiment 2: decentralized with self-coordinating agents).

Results

The experiments yielded several key findings supporting the distributed systems analogy for LLM teams. First, Amdahl's Law accurately predicted the efficiency gains achievable through division of labor, demonstrating that highly parallel tasks benefited most from increased team size, while serial tasks showed minimal speedup. Observed speedups generally remained below the theoretical Amdahl bound, particularly for GPT-5.2 and Gemini 3-Flash. Second, architectural tradeoffs mirroring distributed systems were observed: decentralized teams exhibited significantly reduced efficiency compared to centralized, pre-assigned teams. This was attributed to higher rates of consistency conflicts (concurrent writes, rewrites, temporal violations) leading to more failed tests, and increased communication overhead (more messages, more idle rounds). Conversely, centralized teams were more vulnerable to stragglers (slow agents delaying overall progress), a problem mitigated in decentralized teams where agents could dynamically pick up unfinished tasks. Finally, deploying LLM teams incurred substantial computational costs, with token usage often outpacing speedup, especially in decentralized teams and for serial tasks in centralized settings.

Implications

The study strongly suggests that distributed computing provides a robust theoretical framework for designing and evaluating LLM teams, enabling the anticipation of their limitations and diagnosis of failures. This framework explains observed scalability limits (e.g., Amdahl's Law) and critical coordination challenges arising from centralized versus decentralized architectures. Future research avenues include extending the framework to tasks with dynamic dependency structures, exploring other scalability laws like Gustafson's and Gunther's, investigating heterogeneous LLM teams, and developing fault-tolerance mechanisms (redundancy, verification, consensus) and scheduling algorithms based on distributed systems principles. Adopting a formal foundation for LLM team architectures is crucial for developing systems that are not only more capable but also predictable, efficient, and responsible at scale, avoiding the propagation of errors, conflicting outputs, and substantial computational costs.