Make AI Control System — Automations in Real Time (No Code)

When you have a growing network of AI agents and automations, you face a new problem. You can no longer see what's happening. I'm talking about actual visibility, where are decisions being made, what depends on what, which automation breaks if I change this one variable. That's the situation I found myself in. And instead of adding more automations to patch individual problems, I decided to step back and build an AI control system in make something that would give me visibility and predictability as the system grows. Most teams manage automation as a list

of disconnected scenarios. You log into make and see a vertical list of workflows, each one looking self-contained and independent. At a small scale, this approach works reasonably well. When you have three or four workflows, you can usually keep the logic in your head. You know which scenarios talk to each other, where the important decisions are made, and which ones you should be careful with when making changes. The system still feels manageable even without explicit visibility. The problem starts when complexity increases. As soon as you introduce AI agents, shared data or conditional routing, the list stops representing how the system actually works. Dependencies become implicit instead of explicit and small changes start having unexpected side effects. At that point, you're no longer managing automation. You're managing complexity without visibility. And that's where things break down. The real issue isn't the lack of automation, but the lack of observability. What teams need is a control layer that makes system behavior understandable before something goes wrong. An AI control system is not a single feature or a single scenario. It's an architectural approach to building automation in a way that stays understandable as it grows. In my setup, this architecture is built

from three scenarios, each with a clearly defined role. One scenario is responsible for making decisions based on shared signals. Another scenario reacts to those decisions and performs actions. The third scenario observes the system and explains what just happened. At the center of all three is a shared data layer. This is where priority values, status flags, and routing conditions live. Instead of spreading decision logic across multiple workflows, everything reads from and when necessary writes to the same source of truth. Visually, make represents this type of architecture with grid. Grid doesn't execute logic or make decisions on its own. What it does is make the structure of the system visible, showing how scenarios connect around shared data and decision points. That visibility is what turns a collection of workflows into a system you can reason about. Let me walk through the decision node in more

detail. I'm opening my core decision scenario. This scenario doesn't send messages, update CRM, or trigger notifications. Its only responsibility is to evaluate the current system state and decide how the system should behave. At the center of this scenario is a shared data record which I refer to as the AI control center. Inside this record, I store a priority value. On its own, this looks like a simple number, but it plays a critical role in the system. Multiple scenarios depend on this value. Some only read it to determine their behavior, while others update it based on new signals. Because this value lives in a shared data store, it becomes a stable reference point for the entire system. The router is where the actual decision happens. If the priority is above a defined threshold, the system follows the escalation path. If it's below that threshold, it follows the normal path. There's no hidden logic here and no implicit behavior. The decision boundary is explicit and easy to inspect. This is the point where control becomes tangible. Before running anything, I already know how the system will respond to a given state.

Now let's look at what happens when that shared state changes. Instead of modifying multiple scenarios, I update a single value in the shared data store. I'm not touching routing logic filters or downstream workflows. I'm changing the system state in one place. When I run the system again, the same signal flows through the same logic. The difference is that the decision node evaluates a different state which leads to a different outcome. The system routes the signal down a different path not because anything broke but because the conditions changed. This is what I mean by a ripple effect. In a larger system the same mechanism could affect alerts, CRM updates, AI agents, reporting or follow-up workflows. The key point is that the impact of the change is predictable before execution. Nothing adapts magically. The system behaves differently because it was designed to do so. And I understand that behavior in advance. That's the difference between automation and control. The second scenario plays a

very different role in the system. This scenario doesn't make decisions and it doesn't contain thresholds or branching logic. Its job is to react to the outcome of the decision made in the control layer. It reads the shared system state and executes actions accordingly. If the system is in a high priority state, it follows one execution path. If not, it follows another. All of the decision-m has already happened elsewhere. This separation is intentional. By centralizing decisions in one place, actionoriented scenarios stay simple. They are easier to scale, easier to debug, and easier to reason about even as the system grows. The

third scenario doesn't perform actions at all. Instead, it observes the system after it runs. This scenario reads the current system state and uses make AI to summarize what just happened in plain language. Rather than digging through logs or execution histories, you get a concise explanation of the systems behavior. As systems grow more complex, this layer becomes increasingly important. Humans lose intuition long before systems stop working. An observer layer helps restore that intuition by translating system behavior into something easy to understand and verify. This isn't automation for execution. It's automation for understanding. This is why visual orchestration is the next evolution beyond traditional automation. You can scale your AI agents without losing clarity. Every decision point is mapped. Every dependency is visible. Every change is traceable. When your workflows are simple, a list view works fine. But when you're building with AI agents, when your system has multiple decision points when data flows through shared objects and triggers downstream logic, you need to see the architecture. You need system level control. Make grid

gives you that. It's not just a prettier interface. It's a different way of thinking about automation. You're not managing tasks. You're orchestrating a system. And that system needs to remain observable as it grows. Here's what I mean. Zoom out with me. This is my full automation network right now. I can see how lead scoring connects to CRM updates, how CRM updates trigger follow-up email sequences, how email engagement feeds back into the scoring model. It's a loop. It's alive and I can see all of it. If I tried to manage this as a list, I'd be lost. But in grid view, it makes sense. I understand the flow. I see the control points. I know where to make changes without breaking things. That's the promise. Build systems that remain controllable as they grow. When you combine these three layers, decision, reaction, and observation, you're no longer building isolated automations. You're building a system. A system where decisions are made once, actions are consistent, and behavior is visible. This is what allows AIdriven workflows to scale without turning into something fragile or unpredictable. You can't control what you can't see. And visibility isn't just about dashboards. It's about architecture. Make AI agents, share data, and grid give you the tools to build systems this way. If this approach resonates with you, check out the link in the description below and let me know in the comments how many agents you're running right now and what kind of systems you're building. And see you in the next one.