How and when to build multi-agent systems

Multi-agent systems require careful consideration of when and how to implement them effectively. Context engineering emerges as the most critical challenge, requiring sophisticated strategies to ensure each agent has appropriate context for their tasks. Systems focused on reading tasks (like research) are generally easier to implement than those emphasizing writing tasks, as read actions are more parallelizable and less prone to conflicting outputs. Production reliability requires durable execution, comprehensive debugging tools, and proper evaluation frameworks. Multi-agent architectures work best for breadth-first queries with high parallelization potential and tasks valuable enough to justify increased token costs.