Before you add planners, crews, or graph-shaped orchestration, build the part that decides what the model should actually see. In this first post, we’ll start an enterprise support copilot and give it the one capability every future agent depends on: retrieval that doesn’t fall apart in production. In a recent post I made the case that MongoDB can serve as the "brain" of a modern AI application by combining durable state, retrieval, and application data in one place. That framing still holds, but brains are only useful if they can recall the right thing at the right time. I wanted to dig into agentic application development in more detail in a series of posts, so for the first real entry in this series, I want to start one layer below "agents" and one layer above raw storage: the context layer. That might sound slightly less glamorous than "multi-agent orchestration," which is exactly why it matters. Most enterprise AI systems do not fail because they lack a clever planner.…