A new technique called Gliding Horse addresses a fundamental challenge in AI agent development: maintaining context relevance over long interactions. The system employs a RelevanceTracker that scores context elements on two dimensions—immediate utility and long-term importance—then applies an L1 eviction strategy to prune low-value information. A ContextWindowManager further compresses the active window based on perceived relevance, while a background coherence analysis ensures topic continuity. This approach is particularly valuable for agents that must sustain coherent behavior across many turns, such as customer support bots or research assistants. The technical depth suggests the author has implemented and tested this in a real system, making it more than a theoretical proposal. For developers building production agent frameworks, Gliding Horse offers a concrete pattern to manage context windows efficiently without losing critical information.
A new context-aware compression system for AI agents uses dual-dimension relevance scoring and L1 eviction to prevent context drift.