context-fundamentals

Context management involves structuring system prompts, tool definitions, retrieved documents, and message history to shape agent behavior. Design a minimal blueprint with a focused system prompt, clear tool guidance, and selective retrieved documents to control inference and optimize token usage across your agent architecture.

Agent context comprises system prompts, tool definitions, retrieved documents, and tool outputs. Each component influences attention and inference; managing their size and order affects token budget and performance. Understanding this anatomy lets you debug context-related failures and balance completeness against efficiency.

Token optimization requires selective content loading and context budgeting across all components. Estimate token counts for each section, prioritize high-value information, and progressively disclose details only when needed. This approach reduces waste while maintaining the context necessary for accurate agent reasoning.

Large document sets increase context noise and compete for attention budget, degrading reasoning quality. Selective retrieval and progressive disclosure—loading only relevant documents per task—improves focus. Context design principles help diagnose whether the issue stems from retrieval strategy, tool output verbosity, or prompt clarity.

Yes. Tool output and retrieval management are core context components. If an agent ignores or misuses tools, context design reveals whether the issue is unclear tool definitions, insufficient guidance in the system prompt, or competing attention signals from other context components.

Tool definitions must balance clarity with brevity. Document parameters, purpose, and expected outputs concisely; remove redundant or rarely-used details. Situate definitions in system prompt or tool guidance sections and measure their token cost against the attention budget to ensure room for retrieved data and history.