Multi-agent systems allow LLMs to work together like a coordinated team rather than a single, overloaded worker. Each agent takes on a focused role, planning, critiquing, researching, coding, executing, so the overall system can handle tasks that would overwhelm one model acting alone. By distributing cognitive load, enforcing specialization, and enabling agents to cross-verify each other’s work, multi-agent architectures improve reliability, reduce failure loops, and unlock far more complex workflows. When designed with clear roles, proper communication protocols, and the right coordination pattern, a multi-agent system becomes significantly more capable and resilient than any single-agent setup.
