What is context engineering? A guide for AI agent builders

Context engineering is the recurring practice of selecting, organizing, compressing, and retaining the instructions, tools, retrieved material, memory, conversation history, and tool outputs an AI model receives on every call, particularly in multi-step agents. Emerging prominently in 2025 through discussions by Cognition, Shopify’s Tobi Lütke, Andrej Karpathy, Anthropic, and others, it extends prompt engineering beyond instruction wording to managing an entire finite context window. Its central premise is that more input can degrade performance through “context rot,” distraction, confusion, conflicting information, or persistent errors, while large tool schemas and unfiltered web pages can consume substantial token budgets before useful work begins. Common approaches include retrieving information only when needed, compacting old history, clearing obsolete tool outputs, using sub-agents for isolated research, loading tools on demand, and storing structured notes or memory outside the active context. Research such as Agentic Context Engineering proposes maintaining an incrementally curated “playbook,” while Anthropic product features including context editing, memory, tool search, and compaction support these practices. For web research, the discussion argues that query-focused excerpts generally preserve more context capacity than importing full pages, though it also promotes Parallel’s search products as an example of this approach.

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