{"id":104725,"date":"2026-07-13T23:56:12","date_gmt":"2026-07-13T23:56:12","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/104725\/"},"modified":"2026-07-13T23:56:12","modified_gmt":"2026-07-13T23:56:12","slug":"ai-agents-without-context-are-just-guessing-faster-2","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/104725\/","title":{"rendered":"\u2018AI Agents Without Context Are Just Guessing Faster\u2019"},"content":{"rendered":"

\"\"<\/a><\/p>\n

AI agents are only as good as the data they run on. That\u2019s the case project44 CEO Jett McCandless made to FreightWaves CEO Craig Fuller on a recent episode of FW Today. McCandless and Fuller discussed how the freight industry should approach AI: both as an internal efficiency lever and as a customer-facing product.<\/p>\n

project44\u2019s 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.<\/p>\n

Context, says McCandless, is key.<\/p>\n

The foundation of project44\u2019s approach has been to build an engine that orchestrates both first party (via LunaPath acquisition) and third-party agent providers (including Happy Robot and Vooma) and layer them on top of the contextual data project44 already holds for its customers.<\/p>\n

\u201cAI agents without context are just guessing faster,\u201d McCandless said. \u201cShippers have found that there\u2019s a lot of work to try to create context so that those agents can actually be effective.\u201d\u00a0<\/p>\n

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.<\/p>\n

\u201cSome agents performed better at other tasks than others. Language made an impact,\u201d McCandless said. \u201cAnd 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\u2019re really interested in is the outcomes that we drive for them.\u201d<\/p>\n

The practical implication for shippers and LSPs is that the AI layer is already embedded in the platform. There\u2019s no integration project, no prompt engineering, and no custom database work required to get started.<\/p>\n

\u201cBecause it\u2019s already embedded in the platform and it\u2019s clicks, not code, you don\u2019t need prompt engineers,\u201d McCandless said. \u201cYou don\u2019t need to set up these databases because we already have the context. It\u2019s really quite easy and adoption is quick.\u201d<\/p>\n

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.<\/p>\n

When a company dispatches shipments to an LTL carrier, a portion of those shipments won\u2019t 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.<\/p>\n

project44\u2019s agents now handle that workflow automatically, triggered by the company\u2019s API-level dispatch visibility.<\/p>\n

\u201cWe can simply dispatch agents to go and search for that pro number by calling the carrier,\u201d McCandless said. \u201cIt\u2019s a very easy solution, but it\u2019s practical and useful quite often.\u201d<\/p>\n

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.<\/p>\n

McCandless draws a clear distinction between what shippers and LSPs are each looking for from AI-driven intelligence tools.\u00a0<\/p>\n

\u201cWhen we look at the shippers, they\u2019re not primarily interested in reducing the cost of the transportation team,\u201d McCandless said. \u201cThat\u2019s 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.\u201d<\/p>\n

The concept McCandless finds most impactful is what he calls \u201cinfinite labor\u201d: 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.<\/p>\n

\u201cIf they can just improve their supply chain with infinite labor, that\u2019s impactful,\u201d McCandless said.<\/p>\n

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.<\/p>\n

When asked what single problem AI can most meaningfully address in logistics right now, McCandless was unequivocal that the answer is data quality. There are, however, limitations of an agent-only approach.<\/p>\n

\u201cI think the industry is going to realize that it\u2019s good to augment or subsidize or enhance, but it\u2019s never going to replace an EDI or an API in its entirety,\u201d McCandless said.\u00a0<\/p>\n

The nuance matters for shippers. McCandless says that most carriers produce strong structured data, while a middle tier needs moderate agent augmentation, and a smaller segment still depends heavily on agents to fill gaps. The key, he says, is that the platform can now stratify that problem and address it programmatically rather than leaving shippers to manage carrier data quality on their own.<\/p>\n

\u201cEverybody needs better data quality to be able to make better decisions when you\u2019re looking at automating these operational decisions, whether it\u2019s execution or visibility,\u201d McCandless said. \u201cImproving data quality has to be done in conjunction with EDI and API. It can\u2019t be just a substitute.\u201d<\/p>\n

The results of that approach are showing up across project44\u2019s customer base. McCandless says platform data quality has improved roughly two and a half times over the past three years as a function of carrier investment, platform improvements, and the AI augmentation layer working in concert. That improvement has translated into measurable customer satisfaction gains, with the company\u2019s NPS increasing to 44 over that period.<\/p>\n

One limitation that will matter to any shipper or LSP who has to budget for AI-enabled tools is pricing. McCandless acknowledged that project44 itself blew through its internal AI token budget by April, a pattern he says is common across the industry. For many customers, that dynamic creates anxiety around unpredictable costs.<\/p>\n

\u201cA lot of companies are struggling with surprise token invoices,\u201d McCandless said. \u201cWhat they want is some type of pricing and packaging that\u2019s very consistent. Might sound kind of familiar, maybe something like something as a subscription.\u201d<\/p>\n

Agentic Workflow Manager<\/a> is available today and is orchestrating over 53 agents categorized by a series of jobs to be done<\/a>.<\/p>\n

Click here to request a demo of project44\u2019s Autopilot<\/a>.<\/p>\n

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\n AI
\n Supply Chain AI Symposium
\n Jul 15 \u2022 The Old Post \u2022 Chicago, IL\n <\/p>\n

Past the hype. Join operators, founders, and enterprise leaders figuring out how to deploy AI in supply chain.<\/p>\n

The Old Post \u2022 Chicago, IL
\n Register Now<\/a><\/p>\n

\n FreightTech
\n F3: Future of Freight Festival
\n Oct 27 \u2013 Oct 28 \u2022 The Signal at Chattanooga Choo Choo \u2022 Chattanooga, TN\n <\/p>\n

Industry-defining keynotes, rapid-fire technology demos, and industry leaders networking in experiences across Chattanooga – plus the inaugural F3 Awards Dinner featuring the FreightTech and Shipper of Choice reveals.<\/p>\n

The Signal at Chattanooga Choo Choo \u2022 Chattanooga, TN
\n Register Now<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"AI agents are only as good as the data they run on. That\u2019s the case project44 CEO Jett…\n","protected":false},"author":2,"featured_media":104726,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,405,13612,7537,54275,1920,7901,1910,134,718],"class_list":["post-104725","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-ai-agents","tag-ai-and-automation","tag-artificial-intelligence-agents","tag-freightwaves","tag-jett-mccandless","tag-logistics","tag-project44","tag-technology","tag-trucking"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/104725","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=104725"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/104725\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/104726"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=104725"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=104725"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=104725"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}