Context Engineering Is the New Full Stack of AI Agents (zilliz.com)

AI product teams are increasingly treating "context" not as an afterthought but as the core engineering layer for reliable, production-ready agents. The piece argues that while prompts, RAG, big context windows and protocols can each help, they’re often siloed—leaving agents with only ~20–30% of the context they could use. Context Engineering is proposed as the new full stack for agentic AI: an engineered pipeline that unifies system/user prompts, short- and long-term memory, retrieval (RAG), tool invocation, and structured outputs so models get the right facts and capabilities in the right format at the right time. Technically, the discipline centers on three principles—dynamic adaptation, just-in-time assembly, and optimal formatting—and relies heavily on vector databases as the long-term memory layer. Vector DBs store embeddings for semantic retrieval (text, images, audio, structured data), support multimodal queries, enable freshness without retraining, and scale to billions of vectors with low-latency lookups. The story highlights Milvus (and Zilliz Cloud) as a production-grade option—billion-scale indexing, multimodal support, hybrid search (semantic + metadata/keyword filters), and managed deployment—positioning it as the storage/activation engine that turns retrieved context into actionable, stateful agent behavior.