26. Why Systems Become Stable When LLMs Are Placed Outside

How to Build Structures That Do Not Break

tags: [LLM, AI, Design, Mermaid]

🧭 Introduction

In the previous article, we clarified
why systems break when LLMs are placed inside control or decision loops.

The next question is this:

Why does a system become stable when the LLM is placed “outside”?

The answer is not philosophical.
It is a structural issue.

In this article, we visualize—using structure (Mermaid diagrams)—

• What a non-breaking placement looks like
• Why separating layers increases stability
• How far LLMs should be allowed to go

🎯 The Fundamental Non-Breaking Structure

First, here is the conclusion in structural form.

flowchart TD
    subgraph Core[Execution & Control Layer]
        S[Input] --> C[Control / Decision]
        C --> A[Output]
        A --> S
    end

    subgraph Outer[Outer Support Layer]
        S --> L[LLM]
        L --> P[Explanation / Suggestions]
        P --> C
    end

The key points of this structure are clear:

• The control loop is closed
• The LLM is outside the loop
• The LLM does not change results directly

🧱 Why Separating Layers Creates Stability

① Roles Do Not Mix

flowchart TD
    L[LLM] -->|Suggestions| C[Control]
    C -->|Execution| A[Output]

• LLM: language, ambiguity, organization
• Control: state, judgment, execution

Responsibilities do not overlap.

This alone removes major failure causes.

② Responsibility for Correctness Is Explicit

flowchart TD
    L[LLM] --> P[Proposal]
    P --> H[Human or Rules]
    H --> D[Decision]

• The LLM holds no responsibility
• Decisions are always made by another layer

In other words:

LLMs are “intelligence without responsibility.”

By not giving them responsibility, the system becomes safe.

③ Time Scales Are Separated

flowchart TD
    Fast[Fast Loop] --> C[Control]
    Slow[Slow / Asynchronous] --> L[LLM]

• Control: real-time
• LLM: slow, asynchronous

Separating time scales prevents:

• Latency
• Variance
• Uncertainty

from contaminating the control loop.

🛠 Where LLMs Are Most Effective

When placed outside, LLMs can safely handle:

• 📝 Log summarization and explanation
• 🔍 Enumeration of possible root causes
• 🧩 Drafting rules and designs
• 📄 Generating human-readable explanations

They all share one property:

Even if they fail, the system does not immediately break

🚫 What LLMs Should Explicitly NOT Do

Once the structure is clear,
what should not be delegated becomes obvious.

• Final decisions
• Execution triggers
• Safety judgments
• Real-time control

The moment these are handed over,
the system becomes fragile again.

🧠 The Role of Humans

flowchart TD
    L[LLM] --> H[Human]
    H --> C[Control / Decision]

Humans:

• Do not think through everything
• But make the final decision

LLMs outsource thinking
Humans remain the final point of responsibility

This division of labor is the most realistic.

✅ Summary

Breaking structure:
LLM → Decision → Execution

Non-breaking structure:
Control → Execution
      ↑
     LLM (suggestions only)

LLMs are powerful, but
they become toxic when placed incorrectly.

Using LLMs wisely does not mean trusting their capability.
It means questioning the structure in which they are placed.

📌 Series Recap

• 24: What LLMs really are (transformers)
• 25: Structures that break (don’t put them in control loops)
• 26: Structures that don’t break (keep them outside)

With this understanding,
LLMs stop being “scary” and become
tools that can be designed and placed deliberately.