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Effective Context Engineering for AI Agents — Anthropic Engineering
Context engineering is the emerging practice of optimizing the configuration and curation of tokens in language model context windows to achieve desired AI agent behavior, representing an evolution beyond traditional prompt engineering. It addresses the challenge of context rot and attention scarcity by strategically managing all information available to LLMs during inference, including system instructions, tools, external data, and message history.
Built for AI agentsACO · 5238 tokens
Summary
Context engineering is the emerging practice of optimizing the configuration and curation of tokens in language model context windows to achieve desired AI agent behavior, representing an evolution beyond traditional prompt engineering. It addresses the challenge of context rot and attention scarcity by strategically managing all information available to LLMs during inference, including system instructions, tools, external data, and message history.
Tags
context-engineering · prompt-engineering · llm · ai-agents · transformer-architecture · attention-mechanism
Key entities
Anthropic (organization, 0.95) · Large Language Models (LLM) (technology, 0.98) · Transformer Architecture (technology, 0.95) · Context Rot (concept, 0.92) · Prompt Engineering (concept, 0.96) · Attention Budget (concept, 0.9) · Model Context Protocol (MCP) (technology, 0.88)
Classification
analysis · language en · status final
Provenance
claude-haiku-4-5 via @stacklist/be@0.1.0, confidence 0.85, 2 Jul 2026