Enterprise AI's hidden cost is the human labour of verifying and correcting AI output. The fix is a semantic and governance layer that gives models real context.
Most multi-agent systems are fixed-role teams. CORAL lets agents explore, learn and share through memory so they co-evolve, beating evolutionary methods 3-10x.
Fixed-token chunking destroys context and drives RAG retrieval failures. Sentence-level chunking, parent-document retrieval and metadata augmentation fix it.
AI is shifting from prompt engineering to loop engineering. The bottleneck is no longer writing prompts, it's designing autonomous feedback loops that self-correct.
DeepMind's Einstein Test asks if an LLM could have discovered relativity from 1905 knowledge alone. The answer is no, and the reason is abduction.
Anthropic's Natural Language Autoencoders translate a model's internal activations into plain English, making the black box significantly more inspectable.
