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Recently, many AI Agents appear very smart—they can chat, write reports, and generate plans. But when they actually enter semiconductor factories, automotive production lines, or energy systems, they find it difficult to truly participate in production decisions. The reason is not that AI is not smart enough, but that it cannot execute a set of "trustworthy, real-time, closed-loop" actions in the industrial field.

The real barrier for industrial AI has never been "whether it can generate content," but whether it can shorten the handling of an anomaly from hours to minutes; whether it can turn a manual analysis into an automatic system closed-loop; whether it can make a production decision faster, more accurately, and more safely.

And this is exactly what Gtrontec is solving. The real difficulty of industrial AI is not "seeing the problem," but "handling the problem." What the industrial field truly needs is for AI to enter the production process. For example, after identifying an anomaly, AI can prioritize it, then call systems like MES, YMS, EAP, adjust parameters, execute actions within safety boundaries, and take responsibility for the results.

As a leading industrial AI company in China, Gtrontec systematically dissected this issue in a recent speech and provided a path to transform industrial AI from "model capability" to "industrial execution capability."

Industrial AI "acclimatization issues", Why can't even the strongest large model handle an old machine?

Let's look at a real scenario. In an advanced semiconductor wafer fab, hundreds of millions of equipment data records are generated every day. An experienced engineer can quickly identify from a flood of alarm signals which ones are truly affecting yield and which are just noise.

What happens if a general-purpose large model does it? The most common result is either indiscriminate alarming, "bombarding" the engineer with alerts, or missing truly fatal anomalies, causing entire batches of wafers to be scrapped. More complex is that after AI passes the recognition stage, it encounters bottlenecks when it actually "acts"—adjusting process parameters, locking down abnormal machines, calling MES for dispatching, or linking EAP to modify recipes. This is because systems like MES, SPC, and EAP in factories often evolve over decades, with complex interfaces and tightly coupled logic, like a highly customized industrial neural network. Even if AI "understands" the problem, it may not be able to "command" the system.

This is also a key term repeatedly mentioned by He Jun, CEO of Gtrontec, at the 2026 China CIO Summit hosted by IDC: "acclimatization issues." He pointed out that while many AI Agents are already mature in scenarios like customer service Q&A and office collaboration, they quickly expose problems when entering core industrial value chains such as quality anomaly judgment, energy consumption regulation, and production scheduling. The reason is simple: the industrial field demands that AI "be accountable." In an industrial setting, a wrong parameter from AI could mean hundreds of thousands in losses or even a full production line shutdown.

Solving "acclimatization issues," why does Gtrontec prioritize "industrial harness engineering"?

Faced with this problem, Gtrontec did not focus on "training a larger model," but chose a more industrial path: building an industrial-grade harness engineering system for AI to enter real production systems. It addresses whether AI can truly enter the production process, safely call industrial systems, execute closed-loop actions within rule boundaries, and make engineers confident to use it. Essentially, Gtrontec is completing the last mile for industrial AI—from cognition to action.

This system first solves the problem of "language barrier." AI cannot understand the jargon of the factory, let alone where the data is. For example, if you say "yield anomaly concentration analysis," a general-purpose large model does not know: which indicators correspond to yield; from which system the data comes; the relationships between different processes; and which actions should be called.

Gtrontec's approach is to first "semanticize" industrial capabilities. For instance, actions like root cause analysis in YMS, task adjustments in MES, and equipment blocking are broken down into standardized, callable industrial atomic capabilities (Tool & Action).

AI no longer needs to understand complex system interfaces; it only needs to call standardized actions, such as HoldWafer, UnlockEquipment, AdjustRecipe. For the first time, industrial systems become an "industrial tool set" callable by AI.

At the same time, Gtrontec is doing another more critical thing: data semanticization.

Many factories' data is not naturally usable. It is scattered, isolated, lacks business relationships, and even a large amount of "dirty data" has long been deposited in systems. Gtrontec's approach is to build a unified "semantic layer" for data. Core indicators such as yield, OEE, output, equipment status, along with their calculation relationships and business logic, are organized into a knowledge network that AI can quickly retrieve and understand. Only after completing this step does AI truly have industrial context. It no longer just "knows how to chat" but begins to "understand the factory."

The real key to industrial AI is "large and small model collaboration"

After solving the "understanding" problem, a more dangerous issue emerges: hallucinations. Large models are inherently probabilistic, carrying the risk of "convincingly talking nonsense." In an internet scenario, this might just be a wrong answer; but in an industrial setting, it could mean wrong processes, wrong parameters, wrong actions.

The core path proposed by Gtrontec is "dual mode, dual track, dual know-how." Simply put, the large model is responsible for understanding and generation; the small model is responsible for control and execution. The large model (Generative AI) handles understanding natural language; generating reports; providing decision suggestions; calling knowledge bases. The actual industrial control and closed-loop execution are handled by Analytical AI small models. These small models are trained based on industrial mechanisms, historical process data, and physical formulas. They do not aim to "be able to chat" but pursue accuracy, stability, and certainty.

For example, when a yield anomaly fluctuation is detected and the AI Agent decides to adjust etching process parameters, the large model will dispatch a small model trained on industrial mechanisms and real-time data to actually provide parameter adjustment suggestions. At the same time, it retrieves historical cases and process specification documents from the knowledge base as "reference information" for humans. The small model knows the precise relationship between this parameter and temperature, pressure, and chamber status, so the calculated value is trustworthy. The large model is responsible for "assigning tasks" and "searching information," while the small model is responsible for "calculating answers," each doing its job.

This is currently the most realistic, trustworthy industrial AI path that can truly participate in production closed-loop.

In this episode, we discussed the pain points of industrial AI "acclimatization issues" and the way to solve them. So, when AI finally understands instructions, do we dare to let it actually take action? How does Gtrontec enable AI to move from cognition to action in real semiconductor factories? Stay tuned for the next part.