'AI Agents Without Context Are Just Guessing Faster'

project44's Agentic Workflow Manager and the AI orchestration engine underneath it are designed to solve the data quality and operational automation problems that shippers and logistics service providers are dealing with every day, without requiring customers to build anything themselves.

The foundation of project44's approach has been to build an engine that orchestrates both first party (via LunaPath acquisition) and third-party agent providers (including Happy Robot andVooma) and layer them on top of the contextual data project44 already holds for its customers.

"AI agents without context are just guessing faster," McCandless said. "Shippers have found that there's a lot of work to try to create context so that those agents can actually be effective."

Shipment-level data, carrier relationships, dispatch history, historical lane and carrier analytics, and mode-specific workflows are the kinds of contextual information that McCandless sees as the competitive moat. project44 ran multiple agent vendors side by side inside the engine for over a year, benchmarking performance across task types, languages, and communication channels. The takeaway was that no single agent excelled at everything.

"Some agents performed better at other tasks than others. Language made an impact," McCandless said. "And so we focused again on providing the context. We have the distribution and the trust with the customers. When a customer plugs into project44, what they're really interested in is the outcomes that we drive for them."

The practical implication for shippers and LSPs is that the AI layer is already embedded in the platform. There's no integration project, no prompt engineering, and no custom database work required to get started.

"Because it's already embedded in the platform and it's clicks, not code, you don't need prompt engineers," McCandless said. "You don't need to set up these databases because we already have the context. It's really quite easy and adoption is quick."

Dispatch reconciliation, according to McCandless, is one of the practical use cases for this model that has resonated with shippers and LSPs who run LTL freight.

When a company dispatches shipments to an LTL carrier, a portion of those shipments won't return a pro number the following day. The reasons vary and include re-proes, capacity constraints, and pallet size changes at a prior stop, but someone always has to track down the missing information. For decades, that work has been handled by staff or by outsourced teams making manual calls to carriers.

project44's agents now handle that workflow automatically, triggered by the company's API-level dispatch visibility.

"We can simply dispatch agents to go and search for that pro number by calling the carrier," McCandless said. "It's a very easy solution, but it's practical and useful quite often."

The scale is significant. McCandless says that project44 processes roughly 75,000 LTL dispatches per day, an estimated 8% to 15% of the market on any given day. On the day before the interview, agents had matched over 2,000 dispatches that would have otherwise required manual intervention.

McCandless draws a clear distinction between what shippers and LSPs are each looking for from AI-driven intelligence tools.

"When we look at the shippers, they're not primarily interested in reducing the cost of the transportation team," McCandless said. "That's relatively small when you look at how much they spend on freight and the margins that they have overall. What they tend to be interested in is how they can reduce the inventory, and how they can reduce the stock outs."

The concept McCandless finds most impactful is what he calls "infinite labor": the idea that AI agents can absorb the repetitive, high-volume operational work that logistics teams have historically needed bodies to manage. For shippers, that means the ability to improve supply chain execution without scaling headcount, whether the goal is ensuring a flawless product launch, catching potential stock-outs before they materialize, or getting ahead of disruptions that affect parts availability.

"If they can just improve their supply chain with infinite labor, that's impactful," McCandless said.

LSPs whose product is the service itself have a more direct financial calculus. They operate on thin net margins, and the ability to automate carrier communication, data reconciliation, and exception management without adding staff has a direct bottom-line impact.