The Parallelization Trap: Why Running More Agents Simultaneously Often Makes Things Worse

The "Parallelization Trap" describes how increasing concurrent AI agents can paradoxically reduce overall system throughput due to coordination and coherence problems. This happens as agents contend for shared context, leading to stale or conflicting information.