context-budget

Manage context windows for long AI agent conversations by applying progressive disclosure and tiered loading. This framework maintains Core, Domain, and Task contexts, expanding to Full context only on-demand to keep relevant information in memory.

Progressive disclosure handles token budget limits through automatic eviction of low-priority context. It evicts less critical data as conversations evolve, ensuring multi-turn interactions stay within memory limits while retaining active task data.

The best way to balance context and performance during large codebase reviews is using a four-tier context model. Load Tier 1 core data per session, Tier 2 domain context for projects, and Tier 3 task context for active work.

Context management works with other skills through integration hooks to preserve and restore state across tasks. It coordinates with external components to maintain context balance while switching between different workflows.

You need tiered loading for AI agent context management when context can grow beyond limits in complex code tasks. It is applicable to large codebases and multi-turn interactions where context must be balanced with performance.