The AI discussion in logistics is quickly moving beyond copilots, chatbots, and better search. At the Descartes Innovation Forum today, Descartes demonstrated something considerably more interesting: agents working across multiple logistics applications and organizations to execute parts of a representative end-to-end supply chain process.
That distinction matters.
Descartes described one of the persistent problems in logistics as the “swivel chair tax.” A shipment may move physically from origin to destination, but the information required to manage it still moves through transportation systems, compliance applications, carrier portals, email inboxes, messaging platforms, spreadsheets, and people. The applications themselves are often perfectly capable. The problem is everything that happens between them.
Descartes’ emerging answer is what it calls its Agent Control Plane. In the keynote demonstration, an order moved through four representative companies and 15 Descartes products, with 28 individual process steps occurring behind the scenes. Agents performed tasks ranging from compliance checks and information gathering to disruption detection and replanning, while the underlying Descartes applications continued to perform the operational work they were built to do.
But the most important part of the demonstration may have been what the agents did not do. At several points, humans remained responsible for consequential decisions. When a compliance issue appeared, the agent gathered the relevant information and escalated the decision. Later, when a disruption created an opportunity to rebook transportation at an 8% savings, the alternative carrier carried a trust score of just 32. The agent surfaced the option; the human rejected it.
That is a much more realistic model of logistics automation than the idea of simply turning operations over to autonomous AI. Descartes summarized the philosophy particularly well: autonomy is a dial, not a switch.
This is where agentic AI starts becoming operationally interesting. Consider what normally happens when an international shipment is disrupted. Someone discovers the change, someone determines which shipments are affected, and other people begin checking capacity, appointments, customer commitments, carrier options, and downstream consequences. Emails and phone calls start moving among multiple organizations, and by the time the problem is fully understood, hours may have passed.
In the Descartes demonstration, agents detected the disruption, evaluated its downstream impact, investigated alternatives, and coordinated information across the participating companies. Instead of simply alerting the shipper that something had gone wrong, the system could potentially deliver something much more valuable: the problem and the proposed resolution together. That represents a meaningful change in the role of supply chain software.
For decades, enterprise applications have largely waited for people to operate them. Agentic systems introduce the possibility that applications can increasingly initiate work themselves—within defined permissions and with humans inserted at the appropriate decision points. Descartes also emphasized that this is not merely a future concept. The company said it has already executed approximately 3.25 million agent operations and is opening an early-access program for the Agent Control Plane.
Just as important, Descartes is building governance around the model. Agents have identities, actions are logged and attributable, and activity can be reviewed and replayed. That may ultimately prove as important as the AI itself because enterprises will want different levels of human oversight depending on the decision, risk, and business context. They may be very willing to let agents do the investigative work, coordinate routine activities, react to predefined conditions, and bring humans the relatively small number of decisions that actually require judgment.
That was my biggest takeaway from the keynote.
The next generation of logistics automation may not be about removing humans from the process.
It may be about removing humans from all the work they never needed to be doing in the first place.
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