Context Engineering

What is context engineering?

A model's field of view is narrow — it can only see the context you feed it. Context engineering is the careful design of what goes into that window and in what order, so the model sees exactly what matters most.

How is it different from prompt engineering?

Prompt engineering: how to phrase one sentence
It focuses on wording and tricks for a single prompt.
Context engineering: how to build the whole environment
It handles memory, retrieval, ordering, caching and token budgets — the bigger picture. Prompt engineering phrases a sentence well; context engineering builds the whole information environment well.

What problems does it solve?

What to show the model
The context window is limited. Stuff in too much and you dilute the point and burn money. You want to retrieve the most relevant snippets, not dump the whole library in.
In what order
Important first, last, or in the middle? Position changes what the model pays attention to.
How to store memory
Across many turns, which facts should be kept long-term and which can be dropped — that needs a memory strategy.
How to save budget
Every request spends tokens. Trimming and caching smartly brings the cost down.

Why it's an agent's core competency

A complex agent takes tasks, looks things up, makes decisions and returns results all at once — and that rides on context orchestration. Manage context well, and your agent gets smarter, cheaper, and less likely to forget or hallucinate.

Bottom line: context engineering is building the AI a "just right" information environment. Build it well, and the AI can actually perform.