Industry · Logistics

Prioritized action across the network — every change approved.

Agents surface what’s at risk, weigh the options, and route reroutes, reschedules, and discrepancy notes through approval — with a full audit trail.

How the governed layer helps

Control is the default here.

In a TMS or warehouse system, agents turn a noisy exceptions board into a prioritized action list. Read-only status and ETA lookups run automatically, and a comparison card can weigh two reroute options before anything is committed.

Changes that move a shipment or file a discrepancy are approval-gated and audited, so operations stay fast without losing control of what actually changed.

Multi-agent reporting turns a plain question into lane-level analytics — on-time and dwell-time tables plus a flow diagram of the network’s weak links — with no BI tooling.

Every action an agent takes here runs through the same gate: least-privilege permissions, policy and an audit trail, with the sensitive steps held for human approval. The controls behind it are set out on the security and data-handling page.

Example agents & workflows

What it looks like in practice.

Illustrative examples of what a host would configure — tool ids and risk levels are the host’s to define. The primitives underneath are built and tested.

Example

Shipment exception triage

An at-risk table plus a comparison of reroute options; the reschedule is approval-gated before any change is committed.

Example

Inbound receiving discrepancy

A read-only PO-vs-receipt comparison renders a line-item diff highlighting over and short quantities; the discrepancy note is a low-risk create.

Example

Network throughput report

Specialist agents aggregate by lane and return an on-time table plus a mermaid flow of the network — permission-scoped and auditable.

See governed AI for Logistics & supply chain.

Book a demo and we’ll tailor the walkthrough to your sector — the governance model, the two-seam integration, and an example close to your systems.