Context Engineering

Context engineering is the operating discipline behind dependable AI work. Prompt wording matters, but a useful agent or assistant also needs the right business facts, current evidence, relevant history, permitted tools, output format and a clear boundary on what it may assume or do.

For leaders, it is a practical quality and cost control. Too little context produces generic or unsafe answers; too much context hides the important evidence, increases latency and cost, and can expose information a user should not see. The aim is not to give the model every document. It is to give it the smallest authorised set of material that can support the decision at hand.

Concrete example: a contract-review assistant receives the current contract, the approved policy clauses, the customer account scope and the required response template. It does not receive the entire file share or private negotiations from unrelated accounts. The review owner can then inspect both the answer and the evidence route.

Use context engineering as a design question: what must the AI know now; where did it come from; who may see it; how fresh is it; and what should happen when the evidence is missing?