Context engineering

Context engineering has evolved from an optimization tactic into a foundational architectural concern for modern AI systems. Unlike prompt engineering, which focuses on wording, context engineering treats the context window as a design surface and intentionally constructs the AI’s information environment.

As agents tackle more complex tasks, dumping raw data into large context windows leads to "context rot" and degraded reasoning. To combat this, teams are shifting from static, monolithic prompts to progressive context disclosure. Instead of front-loading every instruction and reference an agent might need, these systems start with a lightweight index of what's available. The agent determines what prompts or contexts are relevant and pulls in only what’s needed, keeping the signal-to-noise ratio sharp at every step.

We’re seeing several techniques mature in this space: Context setup leverages prompt caching to front-load static instructions, reducing costs and improving time to first token. Dynamic retrieval goes beyond basic RAG by selecting tools and loading only the necessary MCP servers, avoiding unnecessary context expansion. Context graphs model institutional reasoning — such as policies, exceptions and precedents — as structured, queryable data. Context management techniques use stateful compression and sub-agents to summarize intermediate outputs in long-running workflows.

Treating AI context as a static text box is a fast track to hallucinations. To build resilient enterprise agents, teams must engineer context as a dynamic, precisely managed pipeline.

上下文工程 是在推理过程中，对提供给大语言模型的信息进行系统性设计与优化，以稳定可靠地产出期望结果。它涉及对上下文要素的结构化、选择与编排——例如提示词、检索数据、记忆、指令以及环境信号——以便让模型的内部层处于最优状态。不同于只关注提示措辞的提示工程，上下文工程关注的是上下文的整体配置：即如何组织与传递相关知识、指令以及先前上下文，以实现最有效的结果。 当下，工程师采用的多种不同技术大体可分为三个方面：上下文设置 涵盖诸如使用最小系统提示词、规范的少样本示例以及令牌高效的工具等策略，用于决定性行动。针对长周期任务的上下文管理 通过上下文摘要 和 结构化笔记 来持久化长期记忆，并通过子代理架构 来隔离和总结复杂的子任务，从而应对有限的上下文窗口。动态信息检索 依赖于即时（JIT）上下文检索，其中智能体仅在需要时才自主加载外部数据，从而最大化效率与准确性。