Adaptive Context Pruning for Agentic AI: Techniques, Algorithms, and Implementation
Long-running AI agents exhaust even million-token context windows with file reads, tool outputs, and conversation history. This guide covers the move from naive truncation to adaptive, structure-aware pruning: dependency-graph tracking, hot/warm/cold memory tiers, relevance scoring, and delta-based file caching, with three concrete algorithm designs and a prioritized implementation roadmap.