AI Agent Development for Logistics & Supply Chain

Give an agent the wrong permissions and it becomes a liability; give it the right ones and it absorbs an entire workflow. We spend the first week on that boundary, then build fast against it.

In logistics & supply chain, your data depends on partners whose systems you do not control. That single fact reshapes how ai agent development has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is exception and delay handling, usually integrated against WMS. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.