context-fundamentals

Context engineering involves structuring system prompts, tool definitions, and message history for AI agents. It treats context as a finite resource, emphasizing that context quality ensures accurate task performance rather than simply adding more information.

Context windows and attention budgets limit an LLM by restricting the total information processed simultaneously. Attention mechanics distribute focus across these inputs, meaning excessive or irrelevant data degrades the model's ability to perform tasks accurately.

Design an AI agent system prompt by defining core instructions, then manage memory by structuring message history and tool outputs. This ensures the agent perceives information correctly and operates cost-effectively within its context window.

Progressive disclosure in prompt engineering loads information into the context only when needed. This approach manages the finite context resource efficiently, preventing attention dilution and keeping AI agent operations accurate.

An AI agent ignores instructions when retrieved documents and tool outputs exceed attention budgets. The attention mechanism dilutes focus across too much context, causing the agent to prioritize recent or prominent data over critical system prompts.

Yes, understanding tool definitions is required to build effective AI agents. Tool outputs occupy space within the finite context window, so structuring them properly ensures the agent processes retrieved documents and executes tasks accurately.