What is context engineering, and why does it matter for AI adoption?

Context engineering is the discipline I've been building at Eucalyptus for the past year, and it's the thing most organisations get wrong when they deploy AI.

The default failure mode

Most enterprise AI rollouts look like this: the organisation buys licences, everyone gets access to a chat interface, and three months later usage is low and the early adopters are doing prompting theatre — copying walls of context into a chat box every time they want useful output.

The root cause is almost always the same: the AI doesn't know enough about the organisation to be useful by default.

What context engineering actually is

Context engineering is the practice of systematically designing what your AI knows, and when it knows it. It's not just writing system prompts. It's designing a layered architecture:

1. Identity layer — who is this AI, what can it do, what are the rules?
2. Skills layer — what domain knowledge does it need for specific tasks?
3. Live data layer — what real-time organisational data should it be able to access?

Each layer answers a different question, at a different scope, and should be maintained separately.

Why it changes outcomes

When you get this right, the AI starts every conversation already knowing the organisation's terminology, processes, and constraints. Users don't need to explain what a concept means — they can just ask questions.

At Eucalyptus, we saw this translate into 67% DAU/MAU stickiness — not because we had the best prompts, but because we designed the context so well that the tool was genuinely useful for daily work.