Technical Reference

■ Technical Reference

This section systematically organizes knowledge ranging from
fundamental concepts to practical technologies in AI, machine learning, programming, and DX.

Rather than being a collection of isolated technical explanations,
it is designed as a reference that connects
decision structures (Decision Trace) with implementation technologies.

■ Positioning of This Section

The Decision Trace Model is not just a concept.

It is an implementable architecture that defines:

  • how decisions are structured
  • how they are executed
  • how they are recorded and continuously improved

In this section, each component is explained
along with actual code and implementation examples.

■ Implementable Components

The Decision Trace Model consists of the following technical elements:

  • Ontology (Semantic Definition)
    Defines the meaning of data and concepts, forming the foundation of decision-making
  • DSL (Decision Rules)
    Explicitly describes decision conditions and rules
  • Behavior Tree (Execution Control)
    Structurally controls the flow of decisions and processes
  • Multi-Agent (Role Decomposition)
    Separates and coordinates roles such as evaluation, decision-making, and execution
  • LLM Integration (Signal Generation)
    Utilizes generative AI as input signals for decision-making

All of these components can be implemented as real systems.

■ Core Concepts

Key concepts for understanding decision systems:

Theoretical and design foundations for understanding decision structures

■ Technical Domains

This reference covers the following domains in an integrated manner:

■ What You Will Gain from This Section

  • Knowledge for designing decision structures
  • Methods to integrate AI and rules into real systems
  • Practical patterns applicable to real-world operations
  • A bridge between concepts and implementation
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