How context rot drags down AI and LLM results for enterprises, and how to fix it

Context rot occurs when enterprise AI systems accumulate too much data in LLM context windows, causing conflicting information, hallucinations, and degraded reasoning. As new data is added without removing outdated content, models exhaust their attention budgets and lose accuracy. Elastic's Abhimanyu Anand explains that symptoms include agents looping endlessly, increased latency, and wrong answers. Fixes involve monitoring token consumption and response times, using semantic search tools like Elastic's ELSER, vector stores, and observability platforms to retrieve only the most relevant context. Analyst James Kobielus emphasizes that strong LLMOps governance—including purging stale data before it enters RAG workflows—is essential to prevent a vicious cycle of model drift and diminishing validity.