@zolani_matebese Great question. Most memory systems for agents work by submitting a flat list of notes or filling everything in at a system prompt. The problem is that the noise grows exponentially, and the agent can no longer tell what is relevant to the task at hand.
GPS works differently. When you save a memory, it gets tagged with the specific files, functions, or symbols it’s about. So if there is a rule like “Never log a user ID into this module”, that memory is tied to that file path and related functions. When an agent is about to edit that code, the GPS brings up exactly those memories and nothing else. This context retrieval is based on what the agent is actually touching, not a keyword search or a dump of everything it has learned so far.
The result is that agents have condensed, relevant context right when they need it, rather than a bloated document they mostly ignore.
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