Hierarchical Multi-Agent Systems: Transcending the LLM Context Limit

Single-agent LLM systems collapse under complex, multi-step tasks as context windows fill and attention drifts, wasting API budgets and causing system failures. This article demonstrates how hierarchical multi-agent orchestration—decomposing tasks into specialized, supervised sub-agents—solves this architectural bottleneck. For teams building production AI systems, this pattern is essential for scaling beyond simple use cases to reliably handle real-world complexity.