Prompt engineering is useful, but it becomes a surprisingly small part of the system once an AI feature moves beyond a single request and response. A production agent may have system instructions, conversation history, user preferences, retrieved documents, tool outputs, temporary working state and application metadata competing for the same context window. The engineering problem is no longer simply how to phrase an instruction. It is deciding which information deserves to exist in the model's working environment at a particular moment.
I think about context as a limited runtime resource rather than a giant bucket of text. Static information that rarely changes can live in stable instructions. Product data should usually be retrieved just in time. Large tool responses should be reduced to the information that affects the next decision. Historical conversation can be summarized or compacted instead of being replayed forever. These choices reduce token usage, but more importantly they reduce distraction. Giving a model more information does not automatically make it better informed.
Retrieval also needs purpose. A search result should enter the context because it can change the answer or the next action. This makes metadata, filtering and ranking part of context engineering. If an ecommerce support agent is answering an order question, the latest order state is more important than twenty pages of policy documentation. If the user asks about return eligibility, policy suddenly becomes first-class context.
Memory creates another boundary. Not everything from one interaction deserves to become persistent memory. Durable preferences, stable project information and explicitly saved facts may be useful later. Temporary reasoning, failed attempts and irrelevant conversation should normally disappear. Treating memory as an append-only transcript produces an increasingly noisy agent.
The practical architecture I prefer is a context builder sitting between the application and the model. It receives the current goal, user identity, permissions and available data sources, then assembles only what the model needs for the next step. That layer can be tested independently from the model itself.
The best AI systems are therefore not necessarily the ones with the cleverest prompts. They are the ones that consistently put the right information in front of the model at the right time and keep everything else out of the way.