Modular intelligence: a human-like model for agent orchestration

Human language production provides a valuable framework for constructing modular AI systems by offering distinct stages for reasoning, planning, and execution that mirror external self-monitoring. As AI tasks grow more complex, the demand for modularity increases, allowing for more auditable and diagnosable systems. This approach parallels Levelt’s model of human language production, which breaks down communication into conceptualization, formulation, and articulation stages. By adopting a modular architecture, AI systems can better handle complex tasks through explicit, traceable processes and component-level evaluation, ensuring failures are identifiable and correctable without affecting the entire system. The AI21 Maestro framework exemplifies this by separating reasoning, planning, and execution into distinct components, preserving intent and allowing for external self-monitoring. This ensures that the AI's goals remain clear and auditable, illustrating that modularity in AI, much like in human cognition, enhances system transparency, accountability, and continuous improvement.