Despite the dominance of large language models (LLM), the industry is facing a growing consensus that extensive parameter scaling (scaling laws) has reached a plateau and does not lead to the creation of an AGI. The autoregressive nature of modern systems creates fundamental barriers: irreversible hallucinations, opacity of decision-making (black-box) and the impossibility of continuous learning without catastrophic forgetting. In this paper, we argue that the solution lies not in an increase in computing power, but in a paradigm shift. We formalize the "Engineering Approach to AGI Creation" and present its implementation, Embryo AGI. It is a modular, lightweight cognitive architecture based on an explicit model of the world where knowledge is separated from processing. Unlike stochastic neural networks, our architecture provides: Complete Freedom from Hallucinations: Complete freedom from hallucinations due to strict verification of generation through the knowledge graph and ontology. Single-Iteration & Incremental Learning: The ability to instantly assimilate and integrate new (even contradictory) data in a single iteration, eliminating the need for expensive retraining and preventing the loss of old knowledge. Explainable & Language-Independent Core: Cognitive processes take place at the level of mean-ings, not tokens, ensuring full traceability and independence of thinking from the limitations of natural language. Experiments demonstrate that Embryo AGI is able to maintain a holistic picture of the world and perform logical inference with an accuracy inaccessible to statistical models. We conclude that the transition from token prediction to Narrative Reasoning is a necessary step to create reliable and autonomous AI.