An Organizational Second Brain: Building an AI That

What happened

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 rarely gets captured anywhere durable.

A long-running offline process reasons through source documents and distills them into structured knowledge files – curated statements of how the organization interprets its domain, with constraints, boundaries, and routing implications made machine-readable. Most significantly, that knowledge then forms the basis of a feedback loop that allows the agent to learn from and implement feedback from human experts without the underlying model having to be retrained.

The industry has converged on a similar idea. Andrej Karpathy’s LLM Wiki structures agent knowledge as a navigable graph of files, and Google’s Open Knowledge Format standardizes this for cross-agent interoperability. The shared insight is that knowledge should be pre-extracted, explicitly structured, and progressively disclosed rather than re-derived on every query.

We extended these principles into a system where citation fidelity and institutional consistency are non-negotiable, organizing 200+ files into a strict taxonomy: Every file declares its dependencies (depends_on) and consumers (referenced_by) in YAML frontmatter, forming a bidirectional dependency graph. When one file changes, you can trace exactly what else might be affected, which matters when the self-improvement loop proposes automated edits.

A key architectural decision is how to partition knowledge between the curated wiki and supplementary retrieval (RAG). We split on information density and expected usage frequency. High-density, frequently referenced sources go into the wiki: Distilled files capturing how the organization reasons, such as positions, decision frameworks, boundary examples, and strategic interpretations. The agent consults these on nearly every turn.

Because they encode the organization’s evolving thinking, they need to stay current, and the wiki structure makes them easy to update, version, and validate. Sparse, situationally relevant sources are served through semantic or lexical search (RAG): documents that matter deeply when they apply but are not needed in detail on most runs, such as detailed reference material, individual product specifications, historical decision records, and niche external knowledge.

Why it matters

In compliance domains, for example, the same types of questions can arise across hundreds of product reviews, expert assessments take days of manual research, and inconsistency between assessments creates real organizational risk. It’s not uncommon for experts to spend more time answering routine questions than on genuinely novel and ambiguous work where their judgment matters most.

We need systems that can capture how an organization’s experts reason and make that knowledge available to everyone who needs it, so that expertise is easier to share, build on, and preserve. We set about solving this challenge by codifying institutional intelligence into an AI agent for a specific compliance domain.

The agent combines a knowledge system that acts as the organization’s “second brain,” a reasoning layer that mirrors how domain experts actually think, and an automated improvement pipeline that compounds expert effort permanently. The patterns generalize to any enterprise domain with deep specialist knowledge, whether that is finance, security, or engineering.

Off-the-shelf LLMs provide a strong foundation, but they often need deeper institutional context to be fully effective in specialist domains. ). In high-stakes domains, closing this gap requires supplying the model with the organization’s own knowledge and priorities so its analysis reflects how the organization actually reasons. The system we’ve designed has four layers, each solving a distinct problem: These layers depend on each other.

The knowledge system’s file structure makes automated editing possible. The reasoning layer’s explicit procedures make failure attribution tractable. The evaluation framework gates every change. And the improvement loop feeds back into both knowledge and reasoning. Remove any one layer and the others degrade. Large organizations can accumulate thousands of documents as a byproduct of expert work.

It is tempting to treat those documents as organizational knowledge, but the real knowledge is implicit: how experts reason, what they prioritize, and how they resolve ambiguity. An agent that retrieves document chunks at inference time has to re-derive that reasoning from raw sources on every run, which is slow, error-prone, and inconsistent. We make that implicit knowledge explicit ahead of time.

What to watch

Loading all of them into the wiki would bloat the system and dilute attention. The result is that the agent’s core reasoning is always grounded in the most refined, current organizational knowledge, while it can still reach for supporting evidence when a scenario demands it.