The article examines methods for engineering invariant representations in AI models to support deterministic continual learning, preventing catastrophic forgetting. It proposes an architectural framework termed "AI harnesses" that stabilizes learning dynamics while preserving previously acquired knowledge. The approach leverages invariant feature extraction to ensure consistent performance across sequential tasks.