Compositional Skill Execution in LLM Multi-Agent Systems: A Comparative Study of Collaboration Architectures for Long-Horizon Tasks

Large language model (LLM) agents are increasingly deployed on compositional tasks requiring sequential sub-goal decomposition, yet systematic comparisons of collaboration architectures remain scarce. We evaluate eight agent configurations—four solo models and four multi-agent architectures—across 132 trials on a three-stage compositional skill pipeline under full-context and zero-context conditions. Three findings emerge: (1) architecture choice dominates model choice—a mid-tier team matches a premium solo model in rounds (but at $5\times$ token overhead), while a poorly chosen architecture wastes $55\times$ more tokens than the best baseline; (2) the optimal architecture reverses between information conditions—role pipelines lead under certainty, peer-review loops under uncertainty; (3) expanding inter-agent context from truncated to full history raises multi-agent success from 50% to 100% ($p<0.001$), pinpointing compositional skill handoff as the primary bottleneck.