While a monolithic agent architecture can be effective for well-defined problems, its capabilities are often constrained when faced with complex, multi-domain tasks. The Multi-Agent Collaboration pattern addresses these limitations by structuring a system as a cooperative ensemble of distinct, specialized agents. This approach is predicated on the principle of task decomposition, where a high-level objective is broken down into discrete sub-problems. Each sub-problem is then assigned to an agent possessing the specific tools, data access, or reasoning capabilities best suited for that task.