The Engineering Approach to Artificial General Intelligence

The prevailing paradigm for advancing Artificial General Intelligence (AGI) relies on scaling large language models, primarily based on the Transformer architecture. This approach has achieved remarkable performance on a wide range of tasks by leveraging massive datasets and computational resources. However, these models exhibit fundamental limitations that challenge their viability as a direct path to AGI: they lack robust mechanisms for incremental learning, suffer from catastrophic forgetting, operate as statistical pattern matchers rather than structured reasoners, and are inherently prone to hallucination. We propose a new architectural paradigm, Embryo AGI, that frames intelligence not as an emergent property of scaled data processing, but as a product of deliberate engineering. Our approach dispenses with the monolithic, end-to-end training paradigm, replacing it with a modular, hybrid cognitive architecture. The Embryo AGI framework is built upon several core principles: a universal, language-agnostic internal knowledge representation; a multi-level memory system with mechanisms for knowledge migration and safe forgetting; a hybrid reasoning engine that combines deterministic logic with intuitive and simulative modalities; and agency as an intrinsic property of the system. We demonstrate that this architecture overcomes the aforementioned limitations. It supports true incremental learning from single data points without retraining, ensures verifiable and traceable reasoning paths, and eliminates hallucinations by design through its semantically grounded operations. Prototypes of the system show robust performance in multi-step planning, structured dialogue, and knowledge integration tasks — domains where current large-scale models often fail, thus establishing a viable, engineering-driven path toward AGI.