Context Engineering: The AI Skill That Goes Beyond Writing Better Prompts

Context engineering is an emerging discipline focused on deliberately curating everything a language model receives during an inference call, including system prompts, retrieved documents, conversation history, and tool definitions. Unlike prompt engineering, which focuses on crafting the right words for a single instruction, context engineering addresses the broader challenge of managing information across multi-turn, tool-using AI agent tasks. Three key developments drove its rise: the shift from single-shot chat to looping AI agents, the discovery that larger context windows degrade model performance when filled with loosely relevant content, and the real token costs incurred by production AI systems. Poorly managed context can cause agents to lose track of goals, call incorrect tools, or waste tens of thousands of tokens on irrelevant data before processing the actual user request. As AI agents become more complex and expensive to run, teams are increasingly treating context design as a core engineering problem rather than an afterthought.