Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents”

Building effective Multi-Agent Systems (MAS) requires moving beyond the unstructured "Bag of Agents" approach, which can amplify errors instead of improving results. Success depends on the interplay between agent quantity, coordination topology, underlying model capability, and task complexity. The content proposes a taxonomy of ten core agent archetypes, such as Orchestrator, Planner, and Executor, to provide a structured design framework. These agents are organized into functional control planes within a Multi-Agent Cognitive Architecture, creating a robust system that suppresses errors. This structured methodology transforms a chaotic collection of agents into a high-fidelity, coordinated system capable of solving complex problems.