Context engineering has clear benefits, but it also has limitations. First, it does not make models perfectly reliable. Even with well-managed context, models can still misunderstand instructions or produce incorrect answers. Context engineering reduces failure rates; it does not eliminate them.
Second, context engineering introduces system complexity. Chunking, embedding, retrieval, and summarization require additional infrastructure and maintenance. Teams must handle versioning, re-embedding, and monitoring retrieval quality. While tools like Milvus and Zilliz Cloud reduce this burden, they do not remove it entirely.
Finally, context engineering is constrained by model behavior. Models still have finite attention and imperfect reasoning. If retrieved context is ambiguous or incomplete, the model may still struggle. Context engineering works best when paired with good data hygiene, clear constraints, and ongoing evaluation. It is a powerful technique, but not a substitute for careful system design and realistic expectations.