Context engineering is the practice of deliberately shaping the information environment an AI agent observes at every step—its system instructions, retrieved knowledge, and conversational memory. AI products break in production not because models fail, but because the context around them was never designed. The model is constant; what changes is what it sees. Understanding context as the primary determinant of AI product quality shifts the question from 'what can our model do?' to 'what does our model see?'—and that change is what agentic engineering worked out through practice rather than theory.
I've spent the last year building agents, and this framework names something I've been feeling but couldn't articulate: the model is never the problem. It's the context I give it. Every time an agent breaks, it's because I didn't design its information environment—I didn't tell it what to ignore, what matters, or when to ask instead of infer. This isn't just technical architecture; it's product strategy. The teams that ship AI features people actually trust are the ones who stress-test context failures before launch, who define minimum viable quality in terms of what the model sees, not what it can theoretically do.