The pace of AI development is clearly outstripping the pace at which human society changes.When faced with this reality, discussions often become polarized.Some argue that AI is dangerous and should be stopped.Others believe that AI will become intelligent enough that we should simply entrust decisions to it.However, engineers who work close to real systems often feel uncomfortable with both extremes.AI is neither something that can simply be stopped, nor something that can be fully entrusted with our decisions.The essential question is how we control it.Importantly, the type of control we are discussing here is not PID control or feedback control in electronic circuits.What we are facing today is the control of systems that include intention, responsibility, and social institutions.Why Traditional Control Theory Is No Longer SufficientClassical control theory assumes several conditions.System states are observable.Inputs and outputs are well defined.Objective functions are fixed.Noise can be treated probabilistically.However, once AI is connected to the real world, these assumptions no longer hold.People can misrepresent their state.Decision criteria change over time.Objective functions are rewritten mid-process.Failures can lead to learning, but also to trauma.Responsibility spans technology, institutions, and human psychology.This is not merely a matter of nonlinearity.The control target itself has acquired meaning.As a result, no single discipline of control theory is sufficient on its own.The Domains That Modern Control Theory Must SpanControl theory in the AI era sits at the intersection of several fields.CyberneticsCybernetics treats control and communication as inseparable.The focus is not on issuing commands, but on designing feedback.What matters is not only what an AI outputs, but how that output returns to the system and where it is allowed to stop.Organizational TheoryControl does not end with algorithms.Ultimately, it comes down to how people make decisions and where responsibility is assumed.In this sense, AI control becomes a matter of organizational design.Institutional DesignWhat is technically possible is not the same as what is socially acceptable.Where human approval is required.Where decisions must be returned to humans.Which actions must never be executed automatically.These are not lines of code, but institutions that must nevertheless be designed and implemented.Responsibility TheoryWhen an AI system makes a mistake, who is responsible?Any system that leaves this question ambiguous will not survive in the long term.Control, therefore, is not only about correct behavior, but about fixing the path by which responsibility returns to humans.Cognitive ScienceHumans are not always rational, but they are not entirely irrational either.AI control should not attempt to exclude humans, but should incorporate human cognitive characteristics as a given.EthicsEthics should not be treated as emotional guidance, but as hard boundary conditions.The question is not whether something can be implemented, but whether it must never be executed.Engineering GuardrailsPerfect AI does not exist.What we need is not perfect optimization, but guardrails.The system must be stoppable.It must be reversible.It must be explainable.It must return decisions to humans.This way of thinking is closer to safety engineering than to pure optimization.Control Means Preserving Degrees of FreedomA crucial point is that control does not mean domination.Modern control theory aims to:Preserve freedom.Prevent only catastrophic outcomes.Stop systems before they cross irreversible thresholds.Fully constraining AI will fail.Fully delegating to AI will also fail.Control, therefore, becomes a technology for limiting how systems can fail.MARIA OS as an Implementation ExampleOne concrete implementation of this approach is the design of MARIA OS.AI does not make decisions on behalf of humans.Decisions are decomposed and returned to people.Responsibility is always recorded and traceable.Runaway behavior is stopped by specification.https://os.maria-code.ai/This is not an ethical statement, but a control design.Before making AI more intelligent, we define the boundaries it must never cross.Those boundaries are placed at the intersection of code, institutions, organizations, cognition, and responsibility.What This Means for EngineersThis form of control theory is not limited to AI engineers.It applies to SaaS design.Automation systems.Decision-support tools.Organizational platforms.Large-scale digital infrastructure.Across many domains, technologies are beginning to make decisions that they should not be making.That is why engineers must now ask themselves:Am I working with a technology I cannot control?Am I building a system that cannot be stopped?This is no longer a philosophical question.It is a professional one.ConclusionControl theory in the age of AI is no longer about circuits.It is a form of integrated engineering that spans people, organizations, institutions, responsibility, and technology.Whether AI becomes embedded in society or rejected by it will depend on whether this form of control can be designed.Because technology moves quickly, control must be deliberate.And ultimately, this is a responsibility that only engineers can take on.