Context rot is slowing down your AI agent: How to fix it

Context rot refers to the deterioration in the quality of an AI agent's output over a session as the conversation accumulates too much irrelevant information, such as stale instructions and failed attempts, which competes with critical task data. This degradation is often due to context management issues rather than the model's capabilities. Strategies to mitigate context rot include starting fresh sessions with concise summaries, using smaller and more focused prompts, employing prompt anchoring for critical instructions, and compacting lengthy sessions to preserve essential details while discarding noise. Persistent context files and plan files can help maintain continuity across sessions, while retrieval-augmented generation (RAG) can manage large documentation sets. By treating context as a limited engineering resource, AI agents can perform more reliably, reduce repeated errors, and better adhere to constraints across multiple session tasks.