A running explainer series on AI agent memory. Each post takes one concept, leads with a plain definition, then shows the mechanism with real config and code.
AI agent memory is the layer that lets an autonomous agent keep what it learns, decide what matters, and recall the right thing later. Here is what that means, why a bigger context window is not the same thing, and how the current approaches differ.
Run more than one agent and memory becomes a boundary question. Here is how scope groups in Meaning Memory keep some memories private, share others with a team, and enforce the line in the data model, not the prompt.