context-fundamentals

Agent context optimization involves auditing system prompts, tool definitions, retrieved documents, and message history, then loading only necessary components. Progressive disclosure and token budgeting keep context lean while maintaining inference quality and reducing costs across workflows.

Agent context comprises four components: system prompts that guide behavior, tool definitions that enable actions, retrieved documents that provide knowledge, and message history that tracks conversation. Each should be reviewed and included only when necessary for the task.

Audit each context component systematically starting with system prompts and task descriptions, then progressively reveal additional context as needed. This approach isolates which components cause failures and identifies where context is incomplete or misaligned.

Yes, through token budgeting and progressive disclosure. Start with minimal context—system prompt and task description only—then add components incrementally based on task requirements, eliminating unnecessary documents and history while maintaining performance.

Design scalable architectures by modeling context fundamentals upfront: define minimal system prompts, curate tool definitions, structure retrieved documents efficiently, and manage message history limits. This foundation enables token budgeting and context validation across varying task complexity.

Use progressive disclosure when designing agents handling variable task complexity or operating under token constraints. Reveal context incrementally—starting with core system prompt and task, adding tools and documents only when the agent indicates insufficient context—to optimize both performance and cost.