Quick Answer
A practical framework for evaluating production-grade supply chain agents across context, system integration, decision rights, observability, exception handling, and measurable operational performance.
Agentic AI has moved quickly from concept to enterprise agenda, but supply-chain operations impose a harder test than a demonstration. An agent must work inside real systems, use current operational context, operate within explicit decision rights, expose what it did, and improve a measurable business cycle.
The DataRobot and Supply Chain Now on-demand session makes the production distinction concrete. Its examples span supplier and tariff risk, plant operations, and aftermarket service, including a tariff signal tied to $2.5 billion in revenue risk that was addressed in 72 hours rather than weeks, with human sign-off retained. [1]
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