从 控制 Logic to AI 智能体: 工程 the Handoff Between 自动化 and 不确定性✎ Edit

Most of my work sits at the point where the physical layer meets the software layer. I've built and deployed monitoring and control stacks across marine, mechanical and semiconductor operations, and the pattern repeats in every one of them: engineers spend a large share of their day watching 系统, reading 参数, spotting anomalies, making adjustments, then watching the result again. 流程, pressure, temperature, pumps, valves, resource consumption. The variables change; the loop doesn't.

确定性 automation already covers a lot of that ground. A 阈值 is crossed, the sensor reports it, the controller fires the rule, the actuator moves, and the loop verifies the outcome. That works because the condition was known in advance and somebody wrote the logic for it. 问题 starts at the edge of that logic, where an anomaly shows up that was never in the original context and doesn't match any programmed rule.

That boundary is where AI 智能体 earns its place in the stack. I don't want an agent with its hand on every actuator. The architecture I build toward is the agent helping construct and maintain the operating logic, while the deterministic layer keeps running normal operations and known anomalies on its own. 时间 something falls outside the programmed context, the 系统 escalates it to the agent instead of dropping it on the floor.

The agent then assembles the context it needs, pulling historical data, production requirements, machine behaviour, SOPs and current operating conditions before working out what should happen next. If the resulting action still sits inside the predefined guardrails, it can instruct the 系统 to make the adjustment. If it exceeds its authority or hits a safety 限制, it hands the decision to an engineer or an operator.

The architecture principle is simple: deterministic automation handles what we already know, AI 智能体 handles uncertainty, and guardrails define how far the agent is allowed to act.

Point that at water flow, energy consumption, cooling 系统, compressed air, material usage or machinery efficiency, and AI stops being a chatbot bolted onto the side of the operation. It becomes part of the engineering operation itself.