analiza aiqbench

Meta's architecture for organizational AI agents prioritizes structured, auditable knowledge and self-improving loops over simple retrieval or full model retraining. By separating institutional logic from reasoning processes, this approach aims to preserve deep domain expertise in sectors like compliance and security while ensuring safe application through rigorous testing frameworks. This represents a significant shift toward building specialized, reliable agent systems that integrate seamlessly into enterprise workflows without requiring constant large-scale training.