Meta built an AI system that codifies specialist knowledge into structured files and reasoning procedures, letting the system learn from expert feedback and improve without retraining the underlying model. The approach solves the problem of institutional knowledge trapped in people's heads by making it explicit, auditable, and shareable across an organization.

• The system separates knowledge (what the agent knows) from reasoning (how it thinks), so organizational positions can be updated without touching recipes, and methodology flaws can be fixed without changing knowledge files—this makes failures traceable and updates clean.
• A self-improvement loop compiles expert corrections into permanent updates via a structured wiki of 200+ files with explicit dependencies, letting one expert's fix become institutional memory without model retraining.
• Splitting knowledge between a curated wiki (high-density, frequently used) and supplementary retrieval (sparse, situational) cut token consumption by around 80% per query, improving reasoning quality by delivering only relevant information at each step.