context-fundamentals

Optimize AI agent context usage by organizing system prompts, tool definitions, retrieved documents, and message history to maintain effectiveness within token limits. This skill provides lightweight utilities for progressive disclosure and context budgeting.

Progressive disclosure for multi-agent workflows is a technique to load content on demand. It allows architects to start with minimal context and progressively load documents or tool outputs only as specific tasks demand.

Manage token budgeting for AI agent system prompts and tools by structuring context components and applying validation checks. This approach ensures you build token-efficient environments and handle degradation gracefully in production.

Yes, this approach works for multi-component agent systems with history and documents. It organizes context flow involving system prompts, tools, retrieved documents, and message history to meet validation and budget requirements.

The best way to recover from context limits in production agents is to apply degradation handling and structure checks. By using just-in-time loading and token budgeting, you can efficiently recover from context limits.

You need context budgeting for AI agents when context becomes a finite resource that impacts effectiveness. It is required when designing multi-agent workflows that must load only needed content and avoid exceeding token limits.