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Procedural memory can reduce the operational cost and architectural complexity of AI agents by enabling them to retain and reuse sequences of actions or routines instead of recalculating behavior from scratch. Implementing procedural memory shifts some workload from continuous model inference to stored procedural representations, which can simplify decision flows, decrease redundant computation and streamline agent design choices while preserving task continuity across interactions.