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:

• Decision Trace Model
• Multi-Agent Systems
• Ontology
• DSL (Decision Rules)
• Behavior Tree
• Graph Neural Networks (GNN)

Theoretical and design foundations for understanding decision structures

■ Technical Domains

This reference covers the following domains in an integrated manner:

• Machine Learning
  Models, prediction, feature engineering, and evaluation
• Artificial Intelligence
  Reasoning, knowledge representation, agents, and generative AI
• Programming
  Implementation techniques, design patterns, and architecture
• ICT (Information and Communication Technology)
  System design centered on integrating data, AI, and business processes
• DX (Digital Transformation)
  System design, data utilization, and business transformation
• Life & Tips
  Practical insights, applications, and ways to apply technology in real-world and daily contexts

■ 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

■ Implementation & GitHub

The components introduced in this section are not just theoretical concepts.
All of them can be implemented as working systems.

In the following OSS projects, you can explore how the key elements are realized:

• Definition of decision structures (Decision Trace Model)
• Execution control (Decision Trace Engine / Orchestrator)
• Logging (Decision Trace Ledger)
• Visualization (Decision Trace Viewer)

👉 GitHub

• Multi-Agent Orchestrator Core
• Decision Trace Ledger Core
• Decision Trace Model
• Decision Trace Engine
• Decision Trace Viewer
• Decision Trace Studio

By combining these components,

decision-making is no longer just a concept,
but becomes a reproducible and extensible system.