No Black Boxes: Why I Avoid Make and Zapier for Real Business Systems

This article is not a comparison between automation tools.

I am not here to argue that Make or Zapier are “bad products”. They are widely adopted, well-engineered, and useful in many contexts.

What I want to explain is something more specific:

Why I deliberately avoid black-box automation platforms when building real, long-lived business systems — and why this choice is structural, not personal preference.

The real question is not “how fast can this be automated”

Most discussions around automation focus on speed:

• How quickly can I connect two services?
• How much manual work can I eliminate?
• Can a non-engineer set this up?

Those are valid questions. But they are operational questions, not system questions.

When automation moves from “a task” to “infrastructure”, a different set of questions becomes critical:

• Can I still explain this system six months later?
• When it fails, can I pinpoint exactly where and why?
• If business constraints change, can this system evolve without being rewritten?

These questions are not secondary. They define whether automation becomes leverage or liability.

What Make and Zapier actually optimize for

Platforms like Make and Zapier optimize for a very specific outcome:

Lowering the barrier to automation.

They achieve this by:

• Abstracting execution logic behind visual workflows
• Encapsulating retries, errors, and limits inside platform rules
• Providing prebuilt integrations that “just work”

This is powerful — but it comes with a trade-off that is rarely discussed.

You gain speed, but you give up ownership of reasoning.

You see the flow. You do not own the execution model.

Why black boxes fail under real business pressure

In early-stage automation, black boxes feel harmless.

But once a system must support:

• Business-specific prioritization
• Edge cases that are not documented
• Trade-offs between correctness, cost, and timing

abstraction becomes friction.

When something breaks, you are no longer debugging logic — you are guessing platform behavior.

And worse:

You cannot reason about “why this system behaves this way” without referencing undocumented rules.

At that point, automation is no longer deterministic infrastructure. It becomes probabilistic dependency.

My alternative: Node.js as the reasoning layer

In my systems, Node.js is not used because it is “more technical”.

It is used because it allows me to do one thing clearly:

Encode decisions as explicit, inspectable logic.

Every condition, every branch, every failure path exists as code — not as platform behavior.

This means:

• I know exactly why an action happened
• I know exactly why another action did not
• I can change priorities without rebuilding the system

This is not about “writing more code”. It is about preserving reasoning authority.

Where Google Apps Script fits — and where it does not

In the NSE approach, Google Apps Script is not a replacement for Node.

It plays a very specific role:

GAS is the glue layer.

• Native access to Google Workspace
• Controlled triggers
• Predictable execution constraints

I do not ask GAS to “think”. I ask it to connect, relay, and execute bounded tasks.

Reasoning stays in Node. Integration stays in GAS.

This separation is intentional.

This is not a tool preference. It is a system boundary decision.

I avoid Make and Zapier not because they are flawed, but because they collapse too many layers into one abstraction.

In small workflows, that is fine.

In real business systems, it creates long-term opacity.

Node Systems Engineering exists to preserve one principle:

If a system makes decisions, you must be able to explain them.

That principle cannot survive inside a black box.

This article is part of the Node Systems Engineering (NSE) series.

For the formal definition and framework, see: Node Systems Engineering: Definition & Framework.

Compiled and proposed by DAPHNETXG · December 28, 2025