10/07/2026
The next SCM bullwhip may very likely be machine-made….Yes, by agentic shopping agents. The precedent?...
On May 6, 2010, U.S. markets plunged and rebounded within minutes. A large automated sell algorithm (a.k.a agent) followed volume but ignored price and time. It interacted with high-frequency strategies, liquidity disappeared, and a five-second pause helped restore order.
The "agent" lesson is clear and very plausible for supply chains.
Individually rational algorithms can create collectively irrational systems.
And the question? Who is guarding and architecting against this?
Agentic commerce is moving from demo to transaction at speed.
Shopping agents can read inventory, build carts and complete purchases. Some retailers are opening catalogues and checkout paths. Others are restricting agents over data, fraud and customer-control concerns. Payment networks are developing common ways to identify and authenticate purchasing agents. The approach is inconsistent.
Most commentary sees lower friction, but I see new bullwhip risk potential.
Imagine this loop:
A consumer agent detects scarcity and reserves the same SKU at three retailers.
Retailer pricing agents raise the price.
Promotion agents redirect demand to substitutes.
Replenishment agents treat reservations and order velocity as consumption.
Procurement agents increase POs.
Supplier agents tighten capacity allocations.
The consumer agent cancels two reservations when one ships.
Everything worked as designed.
The overall system did not. A.k.a BULLWHIP
The old, traditional bullwhip amplified demand because information was delayed, batched or distorted.
Today’s new agentic bullwhip could amplify it because information is instant, reactions are correlated, and software acts before the network knows whether the signal is real or not.
It is a credible design risk.
In experiments, independent AI pricing algorithms learned supracompetitive prices without communicating. Supply-chain agents may also learn from one another’s actions, even when no one designed them to cooperate.
The answer is not banning agents. It is adding market-style controls:
• Transaction and value limits by agent, SKU and time window
• Authenticated orders and duplicate-order prevention
• Short reservation expiries and payment proof
• Rate limits, circuit breakers and kill switches
• Separation of searches, carts, reservations, paid orders and fulfilled demand
• Digital-twin stress tests before autonomy
• Cross-agent observability and decision lineage
• Human approval for abnormal volume, price or capacity moves.
A faster order is not value if it creates duplicate demand, expedites excess inventory or propogates markdowns.
Are we building smarter supply-chain decisions, or simply accelerating every participant’s reaction time?
AI for SCM smartly! Be prepared!