context-shredding

You can compress runtime context for AI agents by reducing artifact data size using predefined tiers and templates. This selectively retains or summarizes information to manage the context budget and prevent model context limits.

The available compression levels for managing agent context budgets are 'full', 'summarized', and 'minimal' tiers. These tiers selectively retain or summarize information from artifacts based on current context usage.

You need to reduce artifact data size when an agent must load multiple large predecessor artifacts and the conversation is nearing its token limit. This prevents context window errors in token-constrained environments.

Yes, you can selectively summarize information from artifacts loaded by your agent. The skill compresses artifact data by retaining only the most critical information based on your chosen compression tier.

The best way to handle context management in token-constrained environments is applying tiered compression to artifact data. This manages the context budget by selectively retaining or summarizing information to prevent exceeding model context limits.