CITATION — REFERENCE ENTRY
Effective context engineering for AI agents — Anthropic
- Key
- anthropic-context-engineering
- Authors
- Anthropic
- Type
- post-weblog
- Publisher
- Anthropic
Raw CSL JSON
{
"URL": "https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents",
"type": "post-weblog",
"title": "Effective context engineering for AI agents",
"author": [
{
"literal": "Anthropic"
}
],
"accessed": {
"date-parts": [
[
2026,
5,
23
]
]
},
"language": "en",
"publisher": "Anthropic"
}
Claims
-
Anthropic frames context engineering as the natural progression of prompt engineering, describing it as the set of strategies for curating and maintaining the optimal set of tokens during LLM inference, including information that arrives outside the prompts.
"Context engineering refers to the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference, including all the other information that may land there outside of the prompts."
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