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@gerardsans

@akoratana A small caveat: agents rely on models that don’t reason or deliberate. Their outputs come from sampling probability distributions learned during training and conditioned on context during inference. The mechanism is statistical density, not logical inference or causation. Traces don’t create new signal, the probability landscape is already fixed. Reinforcement learning simply sharpens the same distribution. Without new external signal, the system is just iterating over its own statistics.

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