Author: Vincent Shing Hin Chong (Vince Vangohn)
Version: v1.1
Date: April 2025
Semantic Stable Agent (SSA) is a minimalistic, language-native AI agent designed based on the principles of the Semantic Logic System (SLS).
It demonstrates how a closed-loop modular agent can operate purely through structured natural language, without external memory, plugins, or API reliance.
This project showcases how Layered Prompt Architectures (Meta Prompt Layering - MPL) and Intent Layer Structuring (ILS) can create long-term semantic stability for agents.
- Copy the Semantic Stable Assistant v1.1 prompt.
- Paste it into any capable LLM (ChatGPT-4 / Claude-Opus / etc.).
- Begin interaction.
The agent will self-regulate tone, logic, and structure automatically.
- Native language-only agent architecture
- Internal semantic reflection and self-correction
- Sustainable tone, rhythm, and task continuity
- No memory or external function dependency
SSA is an application example derived from the Semantic Logic System (SLS),
a modular semantic control framework authored by Vincent Shing Hin Chong.
You can explore the full theoretical foundation here.
This project is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Language is no longer just communication. It is semantic architecture.