Context Engineering
Context engineering is the practice of designing and managing everything a language model sees when it works, including instructions, examples, retrieved data, tool results and memory, so it produces reliable output.
Why it matters
A model can only use what is in its context window, and that space is limited. Prompt engineering focuses on the wording of one instruction. Context engineering covers the whole input: which documents to retrieve, which tool outputs to keep or summarize, what memory to carry between steps, and what to leave out so the important facts are not buried.
For go-to-market agents, context decides quality. An agent that drafts outreach needs the ideal customer profile, the offer, banned phrases, a few approved examples and fresh research on the prospect. Too little context leads to generic output. Too much irrelevant context can confuse the model. Teams improve results by testing which inputs change the output, not only by rewording prompts.
Example
A team builds an agent that writes first lines for cold emails. Instead of one long prompt, it assembles the context for each prospect: a short style guide, three approved example emails, the prospect's recent company news pulled from a search tool, and the CRM note from any past call. Unrelated history is dropped before each run.
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