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In this post, I would like to think about agents that already run our world. They are not the fancy agents that have come to dominate the technology discourse. They are time-tested workhorses. To understand the true challenge of creating a trustworthy autonomous system, I propose to think about the most successful and widely deployed agent in history: the Proportional-Integral-Derivative (PID) controller. I choose the PID controller precisely because it is simple, real, and tested time again in the real world. It is a decision-making policy in its purest form. While the control algorithm itself is well-studied, the enormous amount of engineering required to make it operate reliably highlights the critical gap between an abstract algorithm and a functioning agent. Analyzing these necessary steps provides a crucial reality check for any credible claim about autonomy. ...

Apple’s recent research article, The Illusion of Thinking, made waves. But the reaction quickly moved beyond the technical. For some, it confirmed LLM limitations; for others, it was inconsequential and human-level intelligence is inevitable. And so the cycle continues: one side dismisses, the other inflates. This misses the point. The real question isn’t who’s right about the future, but whether these technologies are useful now, and how they might become more useful tomorrow. As an engineer, I think we should spend less energy predicting what these systems will become and more time evaluating what they allow us to do today. ...

While working on algorithms, I often thought about how to demonstrate their utility. A bench-scale setup is unlikely to capture many aspects of the complexity of industrial systems. It is too idealized and overlooks important interactions among units. Mathematical models, by contrast, can simulate much more. They can incorporate nonlinear behavior, multivariable dynamics, feedback, and uncertainty. In theory, a well-constructed simulation that captures these complexities should be more informative than an idealized lab setup. ...

The Core Problem: Optimization is Hard Plants are Dynamic: Chemical processes rarely sit perfectly still. Raw materials change, equipment ages, customer demand shifts, and unexpected disturbances occur. Trying to find the single “best” steady operating point isn’t always effective because the plant is always changing. Traditional Models are Complex: For decades, engineers have used mathematical models to understand and optimize these processes. Methods like Real-Time Optimization (RTO) try to calculate the best settings based on these models. However, creating accurate dynamic models (models that capture how things change over time) for complex, interconnected plants is incredibly difficult, time-consuming, and expensive. Keeping these models up-to-date is also a major challenge. Resistance to Change: Implementing complex new optimization systems based on these models faces resistance due to cost, complexity, and uncertainty about whether they’ll actually work reliably in the real world. The Proposed Solution: Learn Directly from Experience (Data) ...