Context engineering is the new backend, the AI memory is the problem

Today we are entering the era of context engineering, and this will probably become the most important discipline in AI-powered software. When large language models first exploded into mainstream development, the dominant skill was prompt engineering. Clever phrasing felt like magic and a few well placed instructions could transform mediocre output into something astonishingly coherent. But that phase was never sustainable. Prompts are surface level and the visible tip of a much deeper architectural iceberg. What actually determines the intelligence of an AI system in production is not how you ask a question but what the system knows at the moment you ask it. This is the key, that knowledge is context and context is architecture. Even with dramatically expanded token limits, context windows remain finite. And more importantly, they are fragile. Add too much irrelevant information and the model becomes distracted, compress too aggressively and you lose nuance, inject contradictory instr