Meta has outlined an “organizational second brain” architecture for agents that need to preserve specialist knowledge. Instead of placing institutional rules inside model weights, the system stores them in more than 200 structured, version-controlled files and uses explicit procedures to decide what to load and how to reason.
The knowledge layer includes authoritative position files, controlled vocabulary, routing indexes and safety gateways. Separate reasoning “recipes” specify the order of analysis and the decision process. This separation helps reviewers identify whether an error came from missing information, a faulty procedure or genuine ambiguity.
Human checkpoints remain part of the workflow. When an expert corrects a result, the system reduces the feedback to a minimal edit, tests it against previous cases and adds the case to a regression suite before the change is accepted. Meta says its compliance deployment cut individual assessments from days to minutes and recorded no regressions during its improvement cycles, although those results are the company’s own assessment.
The design is relevant beyond compliance because it offers a more inspectable alternative to retraining. Its effectiveness still depends on experts maintaining the files, tests and escalation rules as organizational policy changes.