Context engineering is the practice of giving an AI model the right facts, tools and history for each task, so it can act on how your own business works.
Context engineering is the work of choosing, shaping and delivering the information an AI model sees before it answers or acts. The goal is simple: each task gets the facts it needs, from trusted sources, and nothing that distracts from them.
A model only knows two things: what it learned in training and what is in its context window. Training does not include your customers, your projects or last week's decisions. So for work inside a company, the context window decides whether the model is useful.
Prompt engineering improves the instruction: the wording, the examples, the output format. Context engineering improves the material the instruction works on.
A good prompt cannot fix missing facts. When an agent does not know who owns an account or what the team agreed last month, better wording does not help.
Context for a business task usually has several parts:
Context engineering decides which of these to include, in what order, and how to keep them current. Too little context and the model guesses. Too much and the key fact gets lost.
Search over documents returns text that looks similar to the question. It does not say which source is current or how the facts connect. A context graph stores the people, systems, documents and decisions of a company as connected nodes, with sources and dates. An agent can follow those links to collect exactly the facts a task needs.
At company scale, this becomes an enterprise context layer: one shared source of context that every AI tool can use.
Want to see how East River Labs engineers context for your AI agents? Read about our product, or talk to us.