Why DSL Alone Is Not Enough — Stabilizing Inputs with the Decision Trace Model × Multi-Agent Systems —

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Introduction

Large Language Models (LLMs) are extremely powerful.

At the same time,
they are inherently unstable systems.

• Outputs vary even with the same prompt
• Interpretations of intent can shift
• Results change significantly depending on context

While prompt engineering and Domain-Specific Languages (DSLs) can provide a certain level of control,
a fundamental problem still remains:

The input itself is unstable.

This raises an important question:

Is it really possible to stabilize AI behavior with DSL alone?

The Role and Limits of DSL

As discussed in
“What is DSL — Designing Rigor for Prompts and the Decision Trace Model,”
DSL plays a critical role in AI systems.

• Defining decision rules
• Structuring decision logic
• Making constraints and boundaries explicit

In other words, DSL defines:

How decisions should be made

However, this comes with a major assumption:

The input is already stable.

But What About Reality?

Real-world user input is:

• Ambiguous in intent
• Lacking sufficient context
• Noisy in language
• Continuously evolving over time

In other words:

Inputs to a structured DSL are not structured to begin with.

Applying DSL under these conditions leads to:

Unstable inputs propagating directly into unstable decisions.

The Missing Layer: Interaction Stability

To address this issue, we need:

An Interaction Boundary layer

Role of the Interaction Boundary

This layer operates before decision-making:

• Interpreting user intent
• Completing missing context
• Detecting ambiguity
• Refining and guiding inputs

In essence:

It transforms raw input into a usable form.

Boundary Control Through Multi-Agent Systems

In a Decision Trace Model × Multi-Agent architecture,
this layer is not a single process but:

A coordinated system of multiple agents

Intent Agent

• Infers user goals
• Interprets ambiguous expressions
• Generates intent candidates

Context Agent

• Supplements missing information
• References past interactions
• Maintains state

Validation Agent

• Validates against DSL rules
• Detects inconsistencies
• Ensures format alignment

Through their collaboration:

The input itself becomes stabilized.

Stability Is Not an Assumption — It Is Designed

In traditional systems:

Inputs are assumed to be correct.

In the world of LLMs:

Inputs are inherently unstable.

Therefore:

Stability is not something to assume — it is something to design.

Practical Approaches to Stabilization

• Feedback loops (confirmation and re-input)
• Progressive clarification of intent
• Interaction design (UI/UX)
• Agent-based correction

Positioning in the Decision Trace Model

Within the Decision Trace structure:

The key shift here is:

Do not treat input as a fact.
Treat it as:

A signal that must be interpreted.

Why This Matters

Many AI systems fail not because of:

• Model performance
• Prompt design

But because:

They ignore the instability of input.

In other words:

It is not that the system cannot make good decisions.
It is that:

Good inputs have not been designed.

Conclusion

DSL is essential.

But it is not sufficient.

To build reliable AI systems:

• Assume inputs are unstable
• Design the interaction layer
• Introduce multi-agent control at the boundary

Ultimately, this is the key insight:

Stability is not a property of input.
It is a property of system design.

Chinoba

Chinoba

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founded by

Masao Watanabe

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