If prompt engineering is what you say to a model, context engineering is what you let the model see. It covers retrieval, summarisation, memory, tool selection, and the structure of the working set the model reasons over.
Most production AI failures are context failures, not model failures: the model was capable, but it did not have the right account brief, the latest brand guidelines, or the constraint that the client cannot mention competitor X.
For agencies, context engineering is the operating model question behind every AI workflow. Where does the context live? Who keeps it current? How does an agent know which slice to load? Get this right and AI becomes a force multiplier. Get it wrong and it produces confident-looking nonsense.