Agentic AI Doesn’t Impress Me Yet. But It Will.

What years of building AI for the grain supply chain taught us about real automation

I recently used one of the new agentic AI tools to plan my attendance at a conference. It did a surprisingly competent job. It sifted through email, found registration details, inferred parts of my schedule, helped me identify relevant sessions and meetings, and turned a messy pile of digital nuggets into something resembling an agenda. As someone who has spent much of his career building systems that turn signals into decisions, I found the experience impressive — yet slightly absurd. The intelligence was there. But the access path was primitive. To reach the content it needed, the agent had to crawl across screens, interpret layouts, read images, and behave like a diligent intern trapped inside my laptop. And that intern had an almost insatiable desire to consume tokens.

This is not a criticism of agentic AI technology in its infancy. It is a criticism of the paradigm under which we are deploying these tools. We are asking AI agents to automate workflows inside digital systems designed for human eyes, human clicks, and human patience. It works, sometimes brilliantly, but it also feels like using the Hubble telescope to read a road sign. Should conference websites ship with MCP connectors already? I am increasingly convinced the answer is yes.

Personal perspective

When we first started designing the Internet-of-Crops® platform almost ten years ago, “agentic AI” was not a term of art. Nor had the world become familiar with World Models as the next frontier in humanity’s pursuit of synthetic general intelligence. Our starting point was more practical. A large share of food waste happens after harvest, in the silent interval between field and processing: inside silos, warehouses, fumigation chambers, vessels, containers, and supply chains still operated with little premeditation and too much habit. Our vision was to make that invisible post-harvest layer measurable, predictable, and ultimately controllable, so that stored commodities could move through the world with less waste, less energy, less chemical overuse, and better economic outcomes.

If we could combine sensor data with simulation, we could do more than simply monitor grain. We could understand what was happening inside a silo: where moisture was moving, where heat was building up, whether insects or mould were becoming active (looking at CO₂ buildup), and whether a fumigation was actually working. From there, we could predict what was likely to happen next and test the best response before acting in the real world. For example, the system could decide when running the aeration fans would protect grain quality while using less energy. With the Internet-of-Crops®, that decision can be recommended to an operator with the tap of a button, or even carried out automatically by the platform.

Video: A fluid dynamics simulation of a grain silo

Missing the point
So this is where much of today’s discussion about workflow automation misses the point:

• In factories, an agent should not watch an HMI screen to infer that a line is drifting out of tolerance. It should query the production model, maintenance history, sensor state, and quality constraints, then simulate whether slowing a conveyor, changing a setpoint, or scheduling downtime protects throughput.

• In supply chains, an agent should not just scrape shipment portals. It should reason over inventory, port congestion, temperature excursions, contract terms, and risk tolerances before proposing a reroute or release decision.

• In medical workflows, the goal should not be an AI that clicks through records faster, but one that can assemble patient context, clinical guidelines, lab test files, and care-team permissions into a safe recommendation pathway.

What this approach teaches us is straightforward:

1. Automation is not the same as teaching software to imitate a user sitting at a keyboard (this is also token-intensive and gets expensive pretty quickly).

2. Real automation requires a model of the domain, trusted interfaces into the systems of action, and a digital model that tells the agent whether the world behaved as expected.

3. The next generation of platforms will expose their internal state cleanly, describe their capabilities in machine-actionable ways, and provide safe rails for autonomous decisions.

4. MCP connectors and similar protocols are not just developer conveniences. They may become the plumbing through which AI stops pretending to be a human user and starts becoming a genuine operational layer.

At Centaur, we came to this conclusion through an objective to eliminate wasteful habits in grain handling. Others will arrive there by reimagining their own path to more efficient workflows. The trajectory may differ, but the conclusion is the same: agentic AI does not need more elaborate ways to look through the window. It needs plumbing built for it.

If you are thinking about agentic AI beyond screens and unleashed inside your plant, or wonder about the future of post-harvest supply chains, write me at sotiris at centaur dot ag. I’d be glad to compare notes.