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from training people, because the knowledge no longer degrades when someone moves role or leaves. It also travels sideways, since the same written rule about defending price in a particular region can govern a quote as well as a replenishment decision, which stops planning and commercial teams maintaining separate and quietly contradictory versions of the same commercial logic. And because every rule carries an owner, a version and a date, a change to how the business decides is visible as a decision somebody made, rather than an adjustment buried in a configuration file and discovered 18 months later by whoever inherits it.
The mathematics matters more, not less
Once a planner stops overriding the system, the mathematics is no longer being quietly corrected by a person before anything happens, which raises the premium on getting it right rather than lowering it. An agent does not compute the answer, it assembles the question and hands it to the engine that can, then applies the written preferences to choose among options that are already feasible. No quantity of context will make an infeasible plan feasible or a poor forecast accurate, and a well-governed rule applied to a weak forecast produces the wrong answer more consistently, and with better documentation.
Planning is also not one question but several, each needing a different kind of mathematics: what will be demanded and with what uncertainty, what to hold and where and in what form, and what is actually feasible against real capacity and materials. Language models supply none of that. They are strong on reach and on context, and weak on precisely what planning needs most, being unable to offer calibrated probabilities, plans guaranteed
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