In late 2025, DHL Supply Chain began rolling out AI agents. Built with the freight automation startup HappyRobot across its transportation operations, they handle driver check-in calls, appointment scheduling, and the high-volume communication that used to sit on dispatchers’ desks. Speaking at the Manifest 2026 supply chain technology conference, DHL Supply Chain’s vice president of integrated transportation, Jennifer Miller, described automated appointment scheduling as a genuine relief for staff buried in routine calls. C.H. Robinson has pushed further on raw automation, running more than 30 autonomous agents that the brokerage says now handle millions of freight tasks a year and have broken past the 90% automation mark on some processes.
What both companies are careful to say, though, is what the agents are not doing. DHL still describes its model as human-in-the-loop for exceptions: employees validate recommendations while automation handles the repetitive monitoring. Brian Gaunt, who leads digitalization for DHL Supply Chain, put it simply: a person can spot a single anomaly, but nobody can watch 200 data feeds for variance at once, so judgment still has to land with a person, even as the watching gets automated. That distinction, between automating the watching and automating the judgment, is what matters for every freight operation trying to figure out where agentic AI in logistics actually fits.
Consider what that looks like on one shipment. A temperature-controlled load carrying a retailer’s holiday promotional stock misses its check-in window outside Nashville at 6:42 AM. The visibility platform flags it within a minute, alongside forty other alerts already sitting in the queue from overnight.
None of that forty is unusual. What is unusual is finding out, without opening six systems, whether this particular delay threatens a delivery appointment the customer has already built a promotion around. That question, not the alert itself, is what eats the morning.
The Alert Isn’t the Problem
Freight operations do not suffer from a shortage of information. Most mid-market and enterprise shippers, carriers, and 3PLs already run some form of AI shipment tracking, some combination of TMS location data, telematics, EDI status codes, and carrier check-in data feeding a dashboard somewhere. The visibility layer can inform an operation in near real time that a shipment’s location or timing no longer matches the plan.
What it cannot tell anyone is whether that deviation matters. A two-hour delay on a load with an eight-hour appointment window is noise. The same two-hour delay on a load feeding a customer’s cross-dock with a fifteen-minute receiving slot is a different problem entirely, and figuring out which is which still runs through a person.
That person has to pull the shipment record from the TMS, check the appointment system for the receiving window, confirm current position and ETA through telematics or the carrier’s portal, check CRM notes for the customer’s tolerance for lateness, and often dig through email for context nobody logged formally. Only then can they judge whether the delay is a minor inconvenience or a commitment at risk.
Multiply that by every exception hitting the queue on a given morning, and it becomes clear why experienced transportation coordinators spend a disproportionate share of their day gathering facts rather than making decisions.
What Changes After the Alert, with Agentic AI in Logistics
This is the point where agentic AI in logistics earns its place, and where it tends to be oversold. It’s a different job than generative AI in logistics, which mostly drafts carrier emails or summarizes shipment paperwork. A useful AI logistics agent does not unilaterally decide what happens to the Nashville shipment. It compresses the investigation.
Applied to that one shipment, an agent with access to the right systems can pull the appointment window, the carrier’s current position, the account’s exception history, and any active commitments tied to that load, and assemble a working picture in seconds rather than the twenty or thirty minutes a person would spend chasing the same information.
From there, the agent does one of two things. Inside pre-approved limits, such as a delay under a defined threshold with no escalation history, it executes the standard response: notify the customer, update the appointment, log the change, and close the exception. Outside those limits, because the account has a complaint history, the delay threatens a penalty, or recovery options carry real cost, it surfaces a recommendation and hands the decision to a person, with the context already attached.
Either way, it records what it found, what it did, and why, providing a complete audit trail that matters just as much for the exceptions it resolves quietly as for the ones it escalates. That combination of narrow authority plus a documented reasoning trail is closer to what buyers mean today by AI logistics agents than the vision of fully autonomous freight management some vendors still imply. Gartner projects that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5% in 2025, and forecasts that more than 40% of agentic AI projects will be scrapped before 2027, mainly over unclear value or weak risk controls rather than weak models. The gap between those two outcomes usually lies in workflow design.
Why Do the Underlying Systems Decide What’s Possible
None of this works without connective tissue; most freight operations have been built only partially. An agent can’t assess appointment risk if the transport management system (TMS) and the appointment scheduler don’t share a common shipment identifier, can’t judge account sensitivity if CRM notes live outside dispatch, and can’t act on carrier data that arrives as unstructured email instead of structured EDI or API feeds. It also needs explicit permission boundaries, defined by the operations team rather than inferred by the model: which actions it can take without a human, what thresholds trigger escalation, and which accounts always require a person. Skip that step, and a well-intentioned pilot turns into a system nobody trusts.
Where to Actually Start
The instinct to build an agent that handles the whole exception queue is understandable and usually wrong. A better starting point is a single, recurring, and expensive exception category, such as missed appointments, prolonged dwell time, temperature excursions, or detention risk. Pick the one that consumes the most experienced attention relative to its actual complexity, and treat it as a defined workflow rather than a general AI initiative.
Before building anything, it’s worth measuring the current state of that workflow: detection-to-action time, how many people typically touch each exception, how much of that time goes to chasing status updates versus resolving the issue, and what the exception actually costs in detention fees, redelivery charges, expedited freight, or missed appointments. Once an agent is live, override rates and escalation volume show whether it’s earning trust or getting routed around.
That discipline matters because the gains are real but concentrated. Accenture research on autonomous supply chains found early adopters reporting 27% shorter order lead times and a 25% rise in labor productivity, though those results came from advanced deployments; most organizations in that research had barely begun the shift. The gap between leaders and everyone else lies in scope and measurement discipline, not in access to better AI.
How an Engineering Partner Closes the Gap
That measurement work also clarifies what’s missing. In most cases, it isn’t a new platform; it’s specific integration and workflow gaps, such as a TMS that can’t talk to the appointment scheduler, carrier data arriving as email instead of EDI, or permission logic that lives in someone’s head instead of in the workflow itself. This is where a partner like Trigent fits.
On the data side, that means building and maintaining the API and EDI integrations between the TMS and the ERP, ELD, CRM, accounting, carrier, and partner systems an exception agent needs to draw from.
On the platform side, it means modernizing or extending an existing custom or cloud-native TMS into something closer to an AI transportation management system (TMS), rather than forcing a rip-and-replace, since most exception-handling logic can sit on top of what’s already running.
On the AI side, it means engineering the orchestration layer itself: the rules engine that encodes permission thresholds, the monitoring that catches an agent behaving outside its lane, and the audit trail that shows why it acted the way it did.
Trigent’s transportation and logistics practice works across shipper, carrier, broker, 3PL, and 4PL operations, with connectors already built for more than 30 OEM and proprietary TMS platforms and active connectivity with over 190 carriers. That matters directly here, because an exception agent is only as reliable as the weakest data feed it depends on. The same team handles the quality assurance, DevOps, and cybersecurity work that turns a promising pilot into something a production operation can trust with customer commitments.
Back to the Load in Nashville
The shipment outside Nashville eventually made its appointment, two hours late, within a window the receiving dock could accommodate. Nobody needed to escalate it. But the coordinator who checked it still spent real time confirming that, time that could have gone to the three exceptions on the same queue that did need a decision.
Visibility told the operation what changed. It did not tell them what the change meant or coordinate the response, and for most freight networks, it still doesn’t. Closing that gap doesn’t start with buying an AI product. It starts with picking the one exception category costing the most skilled attention, mapping what it takes to resolve today, then engineering toward it deliberately, the way Trigent does with logistics and transportation teams: one exception category at a time, on top of the systems already running.
The practical question isn’t whether to pursue AI broadly in freight logistics. It’s narrower: which recurring exception is consuming skilled attention right now because the systems can identify the problem, but cannot coordinate what happens next? That’s the question worth answering before the next visibility dashboard gets bought.
Reference:
https://www.ttnews.com/articles/how-ai-transforming-logistics
https://www.scmr.com/article/dhl-supply-chain-bets-on-data-foundations-robotics-and-agentic-ai-to-drive-growth
