Context Rot: Why AI Agents Perform Worse as Conversations Grow Longer

A phenomenon called 'context rot' causes AI agents to degrade in performance as conversation history accumulates, producing contradictions and ignoring earlier instructions. This occurs because language models treat the entire context window as working memory, with no true persistent recall between calls. Key causes include recency bias in transformer attention, instruction dilution from conversational examples, stale reasoning from outdated facts, and token budget pressure near context limits. Developers can detect context rot by testing instruction-following compliance at increasing conversation lengths, typically seeing failure beyond 10–15 turns. Proposed fixes include rolling context windows with compressed summaries of earlier turns to preserve signal while discarding noise.