Context Engineering (Agent Memory) for AI Agents Starts with Retrieval

Imagine you’re taking an open book exam: you technically have all the answers, but somehow that makes it even harder to find the right one. Where do you even start searching? When do you know that your sources are correct? How do you optimize your time so that you don’t blow all your energy on redundant searches? Finding the right information efficiently isn’t that straightforward. Hand an agent a question and it’s left with a similar predicament: it has a limited context window, so like us humans, it has to retrieve information to complete certain tasks (instead of having everything memorized). When searching, agents struggle with: Inefficient searching: every search costs tokens and time. An agent that searches clumsily (e.g. opens every file just to see what's inside, or keeps rewording the same query) can fill its context with search results before it finds anything useful. Too much context at once: one search that dumps a full data export or a dozen documents fills the context window in a single move, leaving no room for the work itself. Irrelevant or stale context: outdated or off-topic…