A cybernetic perspective on interacting with artificial intelligence
Cybernetics — the science of control and communication — gives us an elegant framework for understanding how to work with AI effectively. The fundamental building block is the regulation loop: a universal diagram describing how a system responds to demands and how feedback helps it reach its goal.
Let's start with the general diagram and its key components. Hover over individual elements with your mouse.
The decision-making component. Based on the control error e(t), it generates a control signal u(t). Its goal is to minimize the difference between the setpoint and the actual value.
A process with its own dynamics that responds to the controller's control signal and produces the output variable y(t).
The set of information about the system needed to predict its future behavior. Fully characterizes the system's current "memory."
The reference input — what we want the system to achieve. It represents the regulation target.
The difference between the setpoint and the actual value: e(t) = w(t) − y(t). It tells us "how far we are from the goal."
The actual output of the system — measured and compared against the setpoint.
The mechanism that feeds output information back to the controller's input, thereby closing the regulation loop and enabling correction.
State-space representation:
Control error:
Surprisingly — or perhaps not — this cybernetic model precisely describes our interaction with AI. Whether you're issuing a simple prompt or orchestrating a complex AI agent, you are always operating within a regulation loop. You just need to name what is what.
| Cybernetics | AI / LLM equivalent | Example |
|---|---|---|
| Regulator | AI Agent / LLM | Claude, GPT — decides, plans, generates |
| System | Artifact under construction / application | Code, document, project — what we are regulating |
| Internal state x(t) | Context window / memory | Conversation, system prompt, RAG context, project state |
| Setpoint w(t) | User goal | "Write me a REST API for user management" |
| Error e(t) | What remains to be done | The gap between current state and goal |
| Control signal u(t) | Generated text / action | Code, tool calls, response |
| Process variable y(t) | Result | Produced artifact, response, project state |
| Feedback | Evaluation & reflection | Test results, error messages, user feedback, self-reflection |
The key insight: the quality of regulation depends on the quality of feedback. The more precise and timely the information the system receives about its output, the better the regulator can correct its actions. In the AI context this means — the more clearly you define the goal, the more structured your prompt, and the more specific the feedback you provide, the more effectively AI "regulates" its output toward your goal.
Cybernetics has a governing law: the quality of regulation is directly proportional to the quality of feedback. Without feedback the regulator "flies blind" — it generates actions but has no idea whether they work. The same holds for working with AI, and this is precisely where most users leave enormous potential on the table.
Open loop (no feedback): Single prompt → response → done. No correction, no iteration. Like driving a car with your eyes closed. Surprisingly, most AI users work exactly this way.
Closed loop (with feedback): Prompt → output → evaluation → correction → better output → … Iterative refinement, where each cycle reduces the error e(t).
"This is wrong, fix X." "Good, but add Y." The most direct and most effective form. The user is the ultimate quality "sensor."
Compiler errors, test results, API responses, terminal output — automatic, immediate feedback from the environment. The foundation of the agentic approach.
The model evaluates its own output in chain-of-thought reasoning: "Does this fulfill the task? Is the code correct? What have I missed?"
Linters, type checkers, CI/CD pipelines, unit tests — structured, measurable feedback. The equivalent of industrial sensors.
1. Speed — Short iteration cycles → faster convergence. Every lost cycle is a lost correction.
2. Specificity — "There is a type error on line 42" is incomparably better than "something is wrong." A precise signal → a precise correction.
3. Measurability — Tests, metrics, acceptance criteria — quantify the error e(t). What cannot be measured cannot be regulated.
4. Close the loop — Don't give up after the first prompt. Iterate. Each cycle reduces the deviation from the goal.
5. Multiplex channels — Combine user feedback + tool results + self-reflection. Multi-channel feedback is more robust.
This is precisely why agentic AI (with tool use and self-correction) dramatically outperforms simple chat — it has a built-in feedback loop. The agent runs code, gets an error, fixes it, runs tests, iterates. Each cycle is one "turn of the regulation loop."
And this is why a structured prompt with clear success criteria is more effective than a vague request — it allows AI to measure the deviation from the goal more precisely and correct its actions more purposefully. You are effectively defining the setpoint w(t) and simultaneously the feedback metric.
In cybernetics, internal state x(t) is critical — without it the system has no memory and cannot learn from previous steps. For AI agents the internal state is the context window, and it is here that they run into a fundamental limitation: context is finite, and in longer sessions compaction kicks in — a lossy summarization of the prior conversation that often drops key details, skips the project configuration, and loses important context.
The situation is reminiscent of the film "50 First Dates" — the protagonist wakes up every day with no memory of the day before. The solution from the film is exactly what you need for working with AI: careful written notes that substitute for lost memory. The researcher in the film had to keep a diary for every day — you need to write structured plans for every step of the project.
In practice this means creating and continuously updating high-quality plans that serve as the "external memory" of the regulation system:
A merger of the specification with the current implementation state. Clear markers for what is done, what remains, what has changed. The plan is updated after every cycle of the regulation loop.
Links to documentation, specifications, and reference projects. The agent can load them when needed — they don't permanently occupy the context window, but are available on demand.
claude.md, .cursorrules, and other configuration files. They define agent behavior for the specific project. Compaction often skips them — they must be concise, precise, and essential.
Plans are not a luxury but an absolute necessity. Without them every longer session degrades into a situation where the agent "forgets" key context and repeats already-solved mistakes. Neglecting plan maintenance pays a steep price over time.
A fundamental insight: an AI agent does not have your eyes. It cannot see the screen the way you can, it has no feel for UX, and it does not intuitively grasp visual context. For it to "see" a given problem or bug, you must creatively design the feedback loop so that the agent can properly understand the error — so it can literally "see" it.
Console output, verbose logs, structured stack traces — a textual form the agent can process. The more structured the output, the more precise the correction.
Multimodal models can process images. A screenshot of an error is often more effective than a verbal description. But the same rule applies here — always supplement with textual context.
Input simulation, test APIs, sandboxed environments. The agent must be able to "touch" the output of its own actions, otherwise it regulates blind.
The user must therefore constantly invent ways to give the agent a quality vantage point — the big picture. Coordinating the entire process toward the goal is a task the agent cannot do on its own. You are the one who sees the whole system from above — and that role is irreplaceable.
Look at the diagram again. A developer who starts working with AI typically struggles because they don't understand their changed role. They describe problems from a developer's perspective, without realizing they now occupy an entirely different position in the regulation loop.
As the regulation diagram shows, the AI user is effectively an analyst, a tester, and part of the control unit — not the executive component of the system. The executive component is the agent. You define the goal w(t), evaluate the output y(t), and provide feedback. You don't write code — you govern the process of its creation.
Input prompts are model-dependent — each model has different strengths and weaknesses, a different "language" it understands best. It is essential to learn to work with the specific model and tool and to understand their peculiarities.
The key principle: you must write prompts so that the model understands the problem through its own eyes, not yours. You must excel at precise, unambiguous descriptions of the problem, the requirements, and the needs. Paradoxically — writers will have an edge over programmers here, because they are accustomed to visualizing through words.
A complete description of the problem, the request, the context, and the needs is an absolute necessity. The system's capabilities, the visualization of the goal, the description of the environment — all of this must be present in the prompt. Laziness in prompting never pays off. It must be practiced like any other skill.
The agent will mark work as done, tests included. But it's like working with developers — it asserts something, but that doesn't make it true. It may not have even run the tests. Occasional "lying" — hallucination — exists and is a property of the system, not a bug. Every output needs independent verification.
Unit tests alone are utterly insufficient as the sole source of feedback. For genuine regulation you need a multi-layered testing strategy:
The foundation — they verify individual components in isolation. Necessary, but not sufficient on their own. The agent will happily write them because they are straightforward.
They verify that components work together — this is exactly where the most insidious bugs hide. The agent will often "forget" about them unless you explicitly steer it.
A quick check that the application as a whole works. Critically important — without them the agent cannot see that one of its changes broke something elsewhere.
Tests that "freeze" verified behavior. Every confirmed piece of functionality must be covered by a test so it isn't lost in the next cycle of the regulation loop.
End-to-end tests are essential — the agent actually runs the application and verifies on your behalf that the core use cases and workflows truly work. They simulate user behavior from beginning to end.
Only taken together do these testing layers give the system "eyes" that understand the application is not finished and the plan is not fulfilled. Without them the agent is blind — and a blind regulator cannot converge.
You may be wondering right now: "But our system can't be tested this deeply — it requires dozens of components, services, and dependencies just to run."
But you are forgetting one key thing: an agent can accomplish an enormous amount of work in a short time — work that would never have occurred to you as a developer because it would have been too costly. Setting up a sandbox, spinning up Kafka, Kubernetes, connecting to dev systems, launching additional services and processes — all of this can take minutes to hours, whereas you would have spent weeks writing it.
Designing the feedback loop for E2E tests is fundamental to the agentic approach. You need to think in an entirely different way than before. The agent will also help you design ways for it to "see" better — but you need to constantly hold the big picture, demand these things, and actively manage them. Otherwise, it defaults to being lazy and will do nothing on its own.
Effective work with AI demands order and system that go far beyond the prompts themselves. Without process discipline even the best model becomes an unpredictable tool.
Work in small, atomic commits. This makes it possible to compare commits with one another and revert to them. Each commit is a "checkpoint" in the regulation loop.
Absolutely essential: maintain clean plans, a bug list, and system settings for the current project. Neglecting this point pays a steep price, especially with long-term use.
Use available projects that solve similar problems. See solutions from multiple angles. The agent can learn from them and adapt proven patterns to your context.
If the model is truly meant to save your time, you need to use the best ones. Free tiers are months out of date or severely limited. There is no working around this fact.
After every piece of agent work, it needs to review its output several times, fill in missing tests, and refine the code. This process follows an asymptotic curve — each iteration yields a smaller quality increment and theoretically could continue indefinitely.
You need to learn to find sensible stopping points — and that depends on how well you understand the model. And how well it understands you. It goes both ways.
For review it is worth using not only your own inspection but also reviews from additional agents who are specifically tasked with reviewing particular parts of the code and architecture. The structure of reviews and their systematicity is one of the most important components of the entire feedback system.
This entire article is itself a demonstration of the cybernetic principle in practice — effective work with AI is not about one perfect prompt, but about sustained governance of the regulation loop: a clear goal, precise feedback, disciplined process, and the continuous refinement of your ability to communicate with the system you are governing.