Developer proposes three-tier progressive knowledge loading for AI agents

On November 11, 2026 at 18:35, a Juejin author published the article 'Agent Dynamically Mounting Knowledge: A Three-Layer Progressive Loading and Routing Mechanism', proposing an approach to how AI agents load and route knowledge. The central argument is that agents should not pack all knowledge into the context upfront. Instead, knowledge should be loaded progressively in three layers: metadata stays resident at all times, body content is injected only when it matches a query, and resources are read only when actually needed. A routing flow, search→inspect→select, sits on top of these layers, so that a large knowledge base can be served with minimal token consumption. The article frames this as a way to cut down on unnecessary context usage while keeping access to large-scale knowledge. So far, no other organizations or authors have published comparable schemes on this mechanism in the available reporting, and no further implementation details or follow-up progress have been disclosed. The account therefore remains at the conceptual description stage, without evidence of deployment, benchmark results, or adoption by other teams.