The common "supervisor" pattern for AI agent orchestration, where a central agent directs specialists, is failing in production environments, typically around the seventh step of a workflow. This occurs because the supervisor's context window becomes saturated, leading to errors like repeating work and losing track of decisions. To address this, teams are shifting towards hierarchical orchestration, which involves layering supervisors to manage smaller groups of agents, isolating complexity and preventing context overload. This architectural change is already yielding benefits like faster development and reduced hallucination rates, as demonstrated by companies like Lyft and Cognition.
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