Context Engineering for Agents

Context engineering manages what information an LLM agent includes in its limited context window at each step, treating the context window like operating system RAM that must be strategically filled with instructions, knowledge, and tool feedback. As agents execute long-running tasks with accumulating feedback, they face problems like context poisoning, distraction, confusion, and clash that degrade performance and increase costs, making context engineering a critical challenge. Solutions fall into four categories: writing context outside the window (via scratchpads and cross-session memories), selecting relevant information, compressing context, and isolating separate concerns.

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