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A new AI agent, the Semantic Stable Agent (SSA), developed by Vincent Shing Hin Chong, is making waves for its innovative approach to self-correction and stable behavior. Built on the Semantic Logic System (SLS) architecture, the SSA achieves this without the need for external memory, plugins, or APIs, relying solely on the power of language.

The SSA’s layered prompt structure is key to its functionality. It establishes a core identity, classifies input to generate relevant responses while maintaining a consistent tone and style, and continuously monitors for semantic drift. When drift is detected, the agent cleverly reinitializes its core identity, effectively resetting itself to maintain semantic consistency during extended interactions.

This unique design mimics complex behaviors such as agentic stability, reflection, and recovery through pure language structuring. Chong believes this approach holds significant promise for revolutionizing long-term dialogue agents, self-correcting AI workflows, and fully autonomous systems driven by language.

The open prompt structure for the SSA is publicly available for testing with advanced LLMs like ChatGPT-4 and Claude Opus. The project repository, including the complete prompt and detailed information about the SLS framework, can be found on GitHub: https://github.com/chonghin33/semantic-stable-agent-sls.

Chong welcomes community involvement, encouraging users to share feedback, test results, and ideas for expanding the SSA’s capabilities, fostering exploration of the potential of language-native AI architectures.