The AI Agent Illusion

Why 90% of Logistics Tech’s Latest Hype Will Collapse

You have seen the demos. You have sat through the pitch decks. You have heard the promises of 24/7 uptime, zero human error, and frictionless automation. But something feels off.

Anthony Miller, a vocal critic of logistics technology hype cycles, recently voiced what many industry professionals think but hesitate to say: most AI agent providers in logistics are selling rebranded workflow automation dressed up in trendy buzzwords. His prediction is stark. More than 90% of these companies will disappear by 2026.

The conversation that followed his post reveals a deeper truth. The logistics industry stands at a crossroads between genuine innovation and another cash grab disguised as revolution.

What You Are Actually Buying

Tim Higham cut through the noise with brutal clarity. He called these solutions what they really are: “If this, then that” triggers. Nothing more than smoke and mirrors.

Miller expanded on this point. Most providers are building trigger-based workflows executed in sequence. An agent spawns, completes a predefined task, throws an exception if something breaks, then disappears. A new agent takes its place. The cycle repeats endlessly.

This approach lacks vision. It lacks strategy. It lacks innovation.

Michael B. identified another critical vulnerability. He noted that 99% of solutions are “closed AI wrappers on workflows dressed up as agents.” What happens when OpenAI raises prices or downgrades processing capabilities? These providers do not own the underlying technology. They cannot control the model, the data flow, or the orchestration. When costs spike or performance drops, their entire business model collapses.

Miller added a darkly humorous detail. Some companies are using n8n (a workflow automation tool) paired with public LLMs. They are not even building proprietary systems. They are assembling off-the-shelf components and charging enterprise prices.

The Compliance Reality Check

Brad Hollister suggested that college students could spin up competitors to leading logistics tech companies between classes. The barrier to entry appears that low.

Miller disagreed, but only partially. You cannot build a Freight Management System between classes. Compliance alone prevents that. You need customs compliance, financial compliance, and accounting compliance. These requirements create real barriers.

However, the AI agent space is different. Many providers are bypassing these complexities entirely. They are targeting narrow use cases that avoid regulatory scrutiny. Quick cash grabs work when customers do not understand the implications for their data security and compliance obligations.

The Real Innovation Gap

Josh Blinick made an important distinction. Tool solutions plug holes. Transformative solutions represent moonshots. Building genuine value is a slog. Most companies want quick fixes. Many thought leaders chase whatever flashy object appears next.

Miller pushed back on one key point. The 90-10 failure ratio is normal when new technology emerges. But this is not new technology. This is the same pattern seen during the OCR wave, when optical character recognition was rebranded as “AI.”

You do not need innovation for innovation’s sake. You need solutions that work and provide the right balance of cost versus value. The 100x ARR valuations circulating right now are ridiculous.

Alfonso Quijano provided technical clarity on why many implementations fail. Companies build CrewAI agents and LangChain agents and attempt to deploy them as true agentic solutions. They quickly realize three problems.

First, these agents do not solve deterministic problems well. Most real business problems fall into this category: structured, rules-based issues. These are not creative tasks like writing or video generation.

Second, people misunderstand tools like n8n. Critics say it is “not agentic,” but the n8n AI Agent node is simply a LangChain model made accessible for a wider audience. That makes it an agent, just one running alongside deterministic workflows.

Third, foundational models in logistics are prohibitively expensive. Even investments of $100 million are not nearly enough. The scale required is far larger.

The correct nomenclature matters:

• Workflows equal deterministic processes

• Agents equal probabilistic processes

• Agentic workflows equal deterministic workflows with agentic capabilities

Most vendors are selling the third category while promising the second.

The Exception Handling Myth

Vlad Nikalayeu tackled a fundamental marketing lie. AI vendors sell “exception handling” as if exceptions are rare edge cases. In logistics, exceptions are not rare. They are hundreds of repeatable scenarios. You do not need AI to handle repeatable scenarios. You need proper workflow design.

He identified the real gains. They come from kernel-level applications, not fragile agents hanging on email threads. Agentic AI will not fix scale. If Maersk cannot handle 1,000 quotes per day via API with one partner, what happens when millions of agents hit the same base tools?

The infrastructure cannot support it. The economics do not work.

Nikalayeu also highlighted blind spots in the current hype. HR transformation, errors and omissions insurance, vendor relationship risks—these factors are simply ignored. When reality hits, companies will face problems they never anticipated.

His conclusion cuts deep. AI in logistics is valuable only when built into the core workflow with strategy, scale, and resilience. Everything else is a cash grab agent circus.

The Scalability Problem

Seth Marlatt identified the root cause. Organizations fail to systematically understand and focus on the jobs they are evolving. Without that foundation, you cannot effectively deploy an agent or AI tool. The opposite is true today. You have hammers looking for nails.

Miller agreed. Companies are motivated by three factors: desire for cost cutting, desire for compliance, and fear of missing out. These three combine perfectly for terrible companies to make money.

This creates a vicious cycle. Bad actors exploit FOMO to sell solutions that do not deliver. When those solutions fail, skepticism grows. Legitimate providers struggle to differentiate themselves from the charlatans.

Jonathan Cook offered a counterpoint. He argued that the structure is correct. These workflows could have been built before AI, but it was prohibitively expensive to write code for each scenario and edge case. Those edge cases were handled by operations teams instead.

The technology is good. Progress is incremental. Many companies have made overpromises, but the underlying approach has merit.

Miller responded with a critical distinction. AI-enhanced data extraction is being mixed in with “AI Agents” that are actually AI-enhanced workflows. The space needs clearer categories. Without them, customers cannot make informed decisions.

What Genuine Value Looks Like

Francine Nielander asked the question that cuts to the heart of the matter: What does AI add that basic automation or coding cannot?

She noted that you would need to prompt an AI super strictly if you want it to be predictable. That defeats the purpose of using probabilistic systems.

She did identify legitimate use cases. Text recognition and interpretation capabilities of large language models are interesting. Translating casual communication like “yeah I’m shipping half of the order tomorrow” into structured EDI messages creates real value. No memory is needed for that. Just good prompting.

Self-improving and self-learning cases remain viable. Cases where randomness or unpredictability is actually preferable might work. But how many of those exist in supply chain? Not many.

Erin O’Leary highlighted a practical problem. You need to be very specific about the problem you are trying to solve. One conversation about a potential use case can spiral. The provider starts selling a bunch of other ways their AI agents can work. The scope explodes.

She also observed that many tools are being built as you go. Providers are figuring it out in real time with your data and your money.

Miller confirmed her suspicions. Some big names in logistics technology are building solutions on the fly using n8n and public LLMs. He knows of three companies doing this right now. The practice is terrible, but the playbook is simple. Promise the moon, deliver dirt. Farm proofs of concept for the logos. Spam those logos on the website. Rinse and repeat.

When will logistics service providers and beneficial cargo owners learn that they cannot trust these repeat offenders?

The Historical Pattern

Thorsten Runge provided valuable perspective. He has lived through several “revolutions.” He now approaches the hype with relaxation.

His list of previous instant revolutions serves as a warning:

The dot-com bubble from 1995 to 2000. Anything with “dot-com” was bound to make millions. Amazon and a few others did. Millions of dot-coms did not.

The Y2K scare. The world was predicted to end. Companies spent billions on the equivalent of Noah’s ark. Nothing happened.

Big Data in the 2010s was supposed to be the key for every company to reinvent itself. No one really knew what big data actually was.

Blockchain is great, but only if you know how to use it.

Quantum computing has not revolutionized anything yet.

All of these technologies (except Y2K) have value. They do not change the world just by existing. You can have a hammer, but if you do not know how to hold it, it will do nothing.

Simon F. compared Miller to the characters in “The Big Short.” The feeling is familiar. He remembered Clarksons shipbrokers’ share price jumping more than 50% on the announcement of launching Clarksons.com. A digital brochure drove that valuation increase.

History repeats because people do not learn.

The Path Forward

Mark Dupuis outlined a different approach. His company is not interested in AI-washing products. Their only mandate for AI is that it must tangibly improve the user’s life. They want growth, but not at the expense of core values.

They are researching where to apply AI strategically. They are targeting their users’ most time-consuming manual processes to create real efficiency and value. Once they have researched, built, tested, and confirmed it works, then they will state it publicly.

Miller warned him. If this is what you are doing, good for you. But everyone else playing the fast track to failure game with overselling and underdelivering will hurt you. Build strong moats. Protect yourself from the noise. Do not use the same messaging they do.

Sergey Patkovskiy posed an interesting question. If you boil it down, any sophisticated AI agent is a combination of well-tuned individual task workflows with guardrails set up. Making agents interact and collaborate together to solve end-to-end complex tasks is where the money is.

Miller disagreed. Agents interacting with each other is technically possible if you consider chaining two workflows together. But collaboration? You will need AGI for that. So yes, AI agents have their uses. But what 90% of companies are pushing out will do way more harm than good.

The Vendor Response

Rahul Y. challenged Miller directly. He argued that calling out hype is fine, but saying “holistic” and “innovation” without metrics or examples is just noise. Show your own holistic vision and strategy with KPIs.

Miller fired back. He warned Rahul to be careful. He noted that DSV is an investor, and they will drop him “quicker than a moldy pack of potatoes” if they figure out the value delivered is not what they hoped for.

What metrics do you want? The metrics that many AI Agent companies use in their investor decks? Those decks never contain like-for-like comparisons between AI Agents and outsourcing, or AI Agents and regular trigger-driven workflow automation.

Miller called out Northbound specifically as an example of the shadiness and hype. They never mentioned AI agents before. Now they have fully embraced the buzzword. What they have built is basically a slightly upgraded version of the workflow engine that CargoWise has had for years.

He expressed respect for the journey Northbound has been on. But trying to dismiss a valid observation as “noise” is the same behavior seen from others during the peak of their hype cycles. Ego will get in the way if you think you are innovative.

This exchange reveals the defensive posture many vendors take when confronted with uncomfortable truths. Instead of engaging with the substance of the critique, they demand metrics they know do not exist in standardized form.

What You Should Do

Ashley Smith expressed her frustration. She made a similar post after perusing the market. It baffles her.

Drew K. asked if Miller really thinks it will take until 2026 for these companies to disappear.

The timeline matters less than the pattern. Hype cycles follow predictable trajectories. Initial excitement. Inflated expectations. Disillusionment. Consolidation. Mature adoption.

You are currently somewhere between inflated expectations and the peak of disillusionment. The crash is coming.

Miller offered clear advice. Do not sleep on AI. But please do your diligence.

That diligence should include:

Ask for concrete examples of deterministic problems solved, not just demonstrations.

Demand transparency about the underlying technology. Are they using proprietary models or public LLMs? Do they control the infrastructure?

Understand compliance implications. How is your data handled? What happens if the vendor goes bankrupt?

Request like-for-like comparisons. How does their solution compare to outsourcing? How does it compare to traditional workflow automation?

Check references beyond the logo wall on their website. Talk to actual users who have run the system in production for months, not just completed a proof of concept.

Understand the exception rate. How often does the system throw tasks back to humans? What is the real efficiency gain?

Evaluate the total cost of ownership. Include implementation time, training costs, maintenance, and the cost of handling exceptions.

The Real Winners

Venkat Ramakrishnan expressed curiosity about the work of the 10% who will survive. That is the right question.

The winners will have several characteristics. They will build kernel-level applications integrated into core workflows, not fragile wrappers around email threads. They will own and control their models, data flow, and orchestration. They will target specific, well-defined problems with measurable ROI. They will be transparent about what their systems can and cannot do. They will have strong compliance frameworks that protect customer data. They will scale efficiently without requiring linear increases in infrastructure costs.

Most importantly, they will survive contact with reality. When the hype fades and customers demand results, these companies will still be standing.

The others? They are already ghosts, as Aiman Nadeem put it. They just have not realized it yet.

The Fundamental Question

Nilanka Pieris suggested an alternative future. Open APIs for forwarders could make AI agents disappear. If all systems can seamlessly connect and transfer data, you drastically reduce the need for agents and data entry.

This could extend to vessel schedules, tracking, visibility data, and more. Each shipping line has its own formats and protocols. If the industry standardizes the entire data flow, many third-party applications become redundant.

Miller asked what agency would enforce such standards. The IMO? They cannot even prevent people from committing fraud.

This exchange highlights a deeper issue. The logistics industry lacks standardization. That creates opportunities for middleware and integration layers. It also creates opportunities for vendors to lock customers into proprietary systems.

Real transformation would require industry-wide cooperation. That level of coordination is unlikely in a fragmented, competitive market.

So the AI agent circus continues. At least until the money runs out.

Your Move

You have a choice. You can join the hype cycle, chase the buzzwords, and hope you pick the right vendor before the crash. Or you can step back, do the diligence, and wait for the dust to settle.

The technology has value. AI can improve logistics operations. But value and hype are not the same thing.

Ask yourself: Am I solving a real problem, or am I buying a solution in search of a problem? Do I understand what I am actually getting, or am I trusting a sales pitch? Can this vendor survive when the market corrects, or will I be left with a dead system and no support?

The answers to these questions matter more than any feature list or demo.

Choose wisely. The 90% who will not are counting on you to make their mistake.