No, Context Rot is not a bug in the traditional software sense. It is an expected limitation of how transformer-based language models process long sequences. The model is behaving as designed: it is optimizing token prediction based on learned attention patterns, not enforcing strict logical consistency or long-term memory guarantees.
Calling it a bug can be misleading because it suggests there is a simple fix, like patching code or increasing the context window size. In reality, simply increasing the context window does not eliminate Context Rot. Even with very large windows, attention dilution and instruction competition still occur. This is why developers often observe Context Rot even when they are “well within” the maximum token limit.
Instead, Context Rot should be treated as a systems design concern. Developers mitigate it through context engineering: re-ranking retrieved content, summarizing older conversation turns, reasserting constraints, and separating long-term memory from short-term context. External memory systems, such as vector databases like Milvus or Zilliz Cloud, help by allowing the application to decide what context is relevant at each step, rather than relying on the model to sift through an ever-growing prompt.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md