context-fundamentals

Context engineering manages the model's context window to maximize performance and control token costs. It covers foundational concepts like context components, attention budgets, and progressive disclosure for scalable agent design.

Optimize token budgets by applying context budgeting and filesystem-based access patterns. This approach manages growing knowledge bases through progressive disclosure, preventing context window overflow in long-running conversations.

Progressive disclosure gradually reveals relevant information to the model as needed, rather than loading the entire knowledge base at once. This manages attention budgets efficiently and controls token costs during agent execution.

Yes, filesystem-based context management supports debugging context issues in AI agents. It provides structured access patterns to isolate and identify problems with attention budgets and context components during architecture planning.

Core components of an AI agent's system prompt context include foundational context elements, attention mechanics, and token budgets. Documenting these components ensures structured context management and optimizes overall agent performance.

Progressive disclosure may not suit AI agents requiring the entire knowledge base loaded simultaneously for complex reasoning. If filesystem-based access patterns introduce latency disrupting real-time operations, consider alternative context budgeting strategies.