Context engineering for shared AI work
Why the quality of the company briefing matters across models, agents, and workflow steps.
Model choice matters, but it cannot compensate for missing or outdated source material. Before comparing agents or models, ask: what company briefing do they share?
Context engineering determines which information is available when an AI tool acts. Shared infrastructure should make the source material current, relevant, structured, and complete enough for the task without copying the entire knowledge base into every prompt.
The context window is a budget
A model's context window is finite. Treat it like a briefing for a new employee before a meeting. Including every available document can bury the decisive sentence, while careful selection makes the relevant facts easier to use.
In practice, that budget is spent across retrieved documents, conversation history, tool outputs and instructions. Each one competes for space. The engineering question is a routing question: which facts earn their place?
Three audits to run this quarter
1. The retrieval audit. Pick ten questions your team asked last month. Trace what your system retrieved for each. Was the answer in there at all?
2. The staleness audit. How old is the newest document your AI can see? If your knowledge base was last synced in March, your AI lives in March.
3. The structure audit. Are you feeding raw PDF text, or chunks with titles, dates and ownership attached? Metadata is the difference between a pile and a library.
Where this goes next
Agent harnesses, reusable skills, memory, and AI employees all depend on this knowledge layer. Fixing it first reduces repeated setup and conflicting answers across tools.