_______________________________________________________________________________________________________ Agentic
AI
told the system. Raise safety stock on frozen lines ahead of a promotion everybody knows is coming, rather than waiting for the forecast to catch up. Trim the buffer on short shelflife dairy, because wastage there costs more than a stock-out. Lower the minimum on a line once a supplier’ s lead time has genuinely improved. Each of those is a sentence, and a planner can now write it into the system in plain English, where it becomes the instruction the agents work to, rather than a note about how the system ought to behave. People write and amend it; agents act on it.
Agents working to rules your team has written
The loop that follows is where the value sits. The agent takes the signal it has read, checks it against the rules the team has written, calls the forecast and the optimizer to work out what the options actually are, and then either acts or does not. Where the answer falls inside the written rules, it redraws the plan and drafts the replenishment or the order for approval, naming the rule it relied on so the planner can see the reasoning rather than infer it. Where it falls outside, it escalates, with the trade-off already evaluated and the scenarios attached, so the person receiving it is deciding rather than starting an investigation. And when that person decides something the rules did not anticipate, the sentence they write becomes part of the instruction set, which is how an override stops being a loss and becomes an input.
One written rule, applied everywhere
Writing a rule down once has a second effect that is easy to miss. It stops being one planner’ s practice and becomes the team’ s, applied identically on every line and in every region by every agent that touches the decision, whether the person who first worked it out is at their desk or not. That is the scaling mechanism, and it is a different proposition
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