Active Working Memory: The Hidden Layer That Shapes What AI Agents Actually Reason On

A DEV Community deep-dive argues that AI agents rely on an 'Active Working Memory' layer assembled by the application before any model reasoning begins. The author draws a parallel to human task preparation, noting that just as a writer opens multiple reference documents before drafting, an agentic system retrieves files, executes tools, and restores state before the model sees its first token. The key distinction made is that models reason while applications assemble — the context window receives the output of that assembly process rather than driving it. Failures commonly attributed to model performance are reframed as failures of context assembly, such as retrieving stale or irrelevant information. This piece is the second in a series building out a broader AI memory stack framework.