{
  "id": "context-engineering",
  "code": "PTL-0072",
  "term": "Context Engineering",
  "aliases": [
    "context management"
  ],
  "category": "agents",
  "definition": "Context engineering is the practice of curating the full set of tokens a model sees at each step, including instructions, tools, retrieved data, memory, and conversation history, to maximize the chance of the desired behavior within a limited attention budget.",
  "description": "The term gained currency in 2025 as agents ran for many steps and the main challenge shifted from wording a single prompt to deciding what information enters and leaves the context window over time, through techniques such as compaction, structured note-taking, and subagents.",
  "example": null,
  "broader": [
    "prompt-engineering"
  ],
  "narrower": [],
  "related": [
    "context-window",
    "agent-memory",
    "retrieval-augmented-generation",
    "ai-agent",
    "lost-in-the-middle"
  ],
  "introduced": 2025,
  "sources": [
    {
      "title": "Effective context engineering for AI agents",
      "authors": "Anthropic",
      "year": 2025,
      "url": "https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents"
    }
  ],
  "url": "https://protologue.com/t/context-engineering/",
  "citation": "Protologue. (2026). Context Engineering. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0072). https://protologue.com/t/context-engineering/"
}