Effective context engineering for AI agents Anthropic

Context engineering—strategically curating and managing the tokens available to language models during inference—is replacing prompt engineering as the core discipline for building effective AI agents, because LLMs exhibit context rot and finite attention budgets that degrade performance as context length increases. Rather than focusing solely on writing optimal prompts, engineers must now iteratively manage all contextual information (system instructions, tool definitions, external data, message history) across multiple inference turns to maintain the agent's ability to focus and recall relevant information. This shift reflects the transition from single-task prompting to building multi-turn agents that must continuously refine what information passes to the model from an expanding universe of potentially relevant data.

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