context-fundamentals

Context management in AI agents involves structuring system prompts, tool definitions, retrieved documents, message history, and tool outputs within attention mechanisms to optimize performance. It ensures models process the right information without exceeding context window limitations.

Progressive disclosure in prompt engineering is a strategy to load information into the agent context only when needed. This approach optimizes computational resource utilization, reduces unnecessary token usage, and prevents performance degradation.

You optimize the LLM context budget by applying context budgeting techniques that limit message history and retrieved documents. Structuring system prompts and tool definitions efficiently ensures the agent operates within context window constraints.

AI agent performance degrades during long conversations because message history and tool outputs accumulate, pushing against context window limitations. This strains attention mechanisms, requiring context management strategies like progressive disclosure to maintain efficiency.

The main components of LLM context windows include system prompts, tool definitions, retrieved documents, message history, and tool outputs. Understanding these elements is fundamental to managing attention mechanics and preventing token overflow.