Keeping AI Agents Grounded: Context Engineering Strategies that Prevent Context Rot Using Milvus

This post details strategies for preventing context rot in long-running AI agent conversations by engineering the context provided to LLMs. It explains the causes of context rot (Transformer O(n^2) complexity, out-of-distribution training data) and presents three main approaches: Just-in-Time (JIT) retrieval, pre-retrieval using vector search (specifically Milvus), and a hybrid approach combining both. The post highlights Milvus's capabilities in supporting hybrid retrieval with vector search and scalar filtering, and its support for incremental updates. It provides guidance on choosing the right approach based on knowledge stability and task predictability.