The Cure for Context Rot

Context rot is the degradation of an AI agent's performance as its context window fills with information during complex, long-running tasks. Current models often use a linear chain of thought, which bloats the context with both relevant and irrelevant data, causing performance to decline. A proposed solution is a system that combines a reasoning model and a runtime to decompose tasks into a tree of subtasks. This system automatically prunes irrelevant information from completed subtasks, keeping the primary context clean and allowing the agent to maintain focus and accuracy over extended operations.