Context Engineering for Agentic Systems: Optimizing the Agent's Mind

This article discusses the importance of context engineering in building reliable agentic systems using large language models (LLMs). It explains how context, rather than just model knowledge, is the key lever for controlling LLM behavior.

Details

The article explains that as LLMs gain larger context windows, the temptation is to use them fully. However, more context doesn't reliably mean better answers - it often leads to higher costs, slower responses, and models losing track of what matters. Context engineering is the discipline of deciding what, how much, and in what form to include in the context, as well as when to leave things out. This is crucial for building reliable agentic systems. LLMs have two main sources of information: their internal knowledge and the provided context. While internal knowledge is fixed, context is the lever developers can actively control. But context can quickly balloon in agentic systems as they go through multiple reasoning, tool calling, and response generation iterations. The article discusses how the Transformer architecture's attention mechanism means every new token introduced competes for the model's 'attention budget', so irrelevant or redundant content can actively degrade performance. Context management is also a cost concern, as each additional token increases the inference cost. The article outlines the various components that fill an agentic system's context window, including prompts, tool definitions, retrieved documents, and conversation history, emphasizing the need to carefully curate this information.