What is context engineering?
Context engineering is choosing and arranging the information an AI model sees for a task so it has what it needs and nothing that distracts it. That information includes instructions, tool results, conversation history and retrieved documents. Anthropic presents it as the next step after prompt engineering, which focuses on writing instructions. For an agent, the selection repeats at every step of the work.
Why it matters to a revenue leader
More information can make AI answers worse. Anthropic treats a model's context as a finite resource and points to research showing that a model's recall of details declines as the amount of text grows. This explains why an AI that reads every email and note on an account can write a weaker deal summary than one given a few well-chosen records. What the AI sees depends on your CRM data and on who decides what to feed it, so context quality is as much a RevOps question as a vendor question.
Example
Fictitious numbersA facility services company's AI deal summaries keep citing a bid the company lost three years ago. The RevOps lead limits the input to the current agreement and the last 90 days of activity. The summaries get shorter, and account managers stop correcting them before forecast reviews.
"What exactly does our AI see when it writes a deal summary, and who decided that?"
Sources
- Effective context engineering for AI agents | Anthropic checked 9 Oct 2026
- Harness, Scaffold, and the AI Agent Terms Worth Getting Right | Hugging Face checked 9 Oct 2026