An Organizational Second Brain: Building an AI That Learns From Experts

We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what the agent knows from how it reasons. A self-improvement loop then compiles expert feedback into verified, regression-tested updates without model retraining. A structured, auditable knowledge architecture separates what the agent knows from how it reasons. A self-improvement loop then compiles expert feedback into verified, regression-tested updates without model retraining. Together, these two layers turn one-off expert corrections into permanent, compounding institutional memory, and the pattern is designed to generalize to other domains governed by retrievable text rather than model weights. This system is saving domain subject matter experts (SME)s at Meta substantial time, allowing them to focus more on the work where their knowledge matters most. Many large organizations have the same problem when it comes to specialist knowledge. While some of it is written down in the form of models, playbooks, checklists, and frameworks, the most valuable specialist knowledge lives in people’s heads and…