Context Engineering: The Discipline Behind What AI Agents See Each Turn

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A technical series on building production-ready agentic AI systems has reached Part 5, focusing on context engineering — the practice of designing what information a language model receives on each API call. The full payload sent to a model typically includes a system prompt, conversation history, retrieved documents, tool definitions, and prior tool results. Because language models are stateless, none of this information persists between calls, so the harness must reassemble the entire context from scratch on every turn. This makes context engineering an ongoing, dynamic discipline rather than a one-time prompt-writing exercise. The article identifies three core components of a well-designed context: the system prompt, conversation history, and retrieved content.

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