This article discusses the power of multi-agent systems in artificial intelligence (AI), where multiple autonomous agents work together to solve complex problems. The challenges of managing these systems are explored, including context and data sharing, scalability and fault tolerance, integration complexity, timely and accurate decisions, safety and validation, and overcoming these challenges requires proven design patterns tailored for multi-agent systems. Four key patterns - orchestrator-worker, hierarchical agent, blackboard, and market-based - are introduced, along with an explanation of how to adapt them to be event-driven using data streaming technologies like Apache Kafka. The article highlights the importance of a shared language, interfaces, and immutable logs in maintaining consistency and reliability across agents, enabling seamless integration into dynamic environments. Event-driven design offers a clear path forward for tackling real-world complexity, unlocking the full potential of multi-agent systems.