Loadsmart Deploys AI Agents to Automate Freight Operations

Freight AI moves from platform to task execution

Loadsmart has released a suite of AI agents designed to complete freight logistics work directly within the transportation management systems enterprise shippers already use, according to details first reported by Supply & Demand Chain Executive.

Unlike traditional freight software that requires data migration and team retraining, the agents integrate via API, EDI, and other protocols to execute discrete tasks: collecting and filing documents, pulling carrier status updates, rebooking dock appointments, retendering failed loads, and updating TMS records. When an agent cannot complete a task, Loadsmart's own freight operations team steps in to resolve the exception.

"Transportation teams have been asked to adopt technology the same way for twenty years: buy the platform, migrate the data, retrain the team, hope it sticks," said Felipe Capella, co-founder and CEO of Loadsmart. "We don't think AI arrives that way. It arrives one task at a time, inside the systems you already run, doing work you can watch it do."

Launch capabilities and customization

The initial agent portfolio covers document collection and filing, tracking and status updates, tender failures and retendering, proactive load audits, claims processing, and scheduling operations. Each agent operates under customer-defined rules and guardrails.

Loadsmart will also build custom agents for any repetitive, high-volume workflow a shipper describes, configuring them to work against the customer's existing technology stack. The company positions the offering as outcome-based rather than tool-based — shippers pay for resolved tasks rather than software licenses.

Why it matters

This approach reflects a shift in how enterprise AI is being deployed in logistics. Rather than asking operations teams to adopt new platforms, vendors are building agents that slot into existing workflows and systems. The hybrid model — AI execution with human exception handling — addresses the reliability gap that has kept many shippers from fully automating critical freight processes. For transportation managers evaluating AI investments, the task-level integration model may lower adoption risk compared to rip-and-replace platform strategies.

The details were first reported by Supply & Demand Chain Executive on September 18, 2026.