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A company brain is a shared knowledge graph that your agents query instead of scraping Slack threads and stale Notion pages. This guide builds one with Worlds, the Wiki CLI, and the wazoo agent skill. Vector-only chunk search returns whatever text is most similar to a query. A company brain returns verified facts: assertions that passed shape validation and carry provenance you can trace to their source.

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Prerequisites

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Steps

1

Create the company brain world

Worlds stores facts in isolated worlds. Create one for the company brain:
The world ID company-brain is public and used in every data-plane request. Choose a descriptive ID so agents can find the right world.
2

Model the core schemas

Define SHACL shape constraints for the entities your company tracks: Person, Team, Project, Decision, Meeting, and Architecture. Wiki validates documents against these shapes before they reach the world.Start a wiki vault:
Add a shape for decisions in brain/shapes/decision.md:
Write an architecture decision record that satisfies the shape in brain/decisions/ADR-004.md:
3

Enforce quality gates in CI

Run the integrity and convention checks before any document syncs into the company brain:
Add both to your CI pipeline so a document with a broken shape or a dangling link cannot reach the ledger.
4

Import verified facts into the world

Export the validated ADR as Turtle, then import it into the world:
The import endpoint accepts Turtle, JSON-LD, N-Triples, N-Quads, and Trig. Every import appends to the chronological ledger, so the brain keeps a complete history of what it knows.
5

Connect agent runtimes

Install the wazoo skill so coding agents can query the brain with native tool calls:
Export the tokens the skill reads:
Claude Code, Cursor, OpenCode, and Gemini agents pick up the skill from their workspace automatically. See the Wazoo skill page for environment setup and update instructions.

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Check for success

Search the brain for the decision you imported:
Then confirm the fact exists in the graph with a SPARQL query:
The search returns the ADR document, and the SPARQL query returns its quads. Both results prove the agent-facing ledger contains verified, queryable facts.

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Use cases

  • Autonomous onboarding: New engineers query the brain for architecture decisions, system topology, and team roles instead of asking in Slack.
  • Verifiable ADR tracking: Agents check accepted decisions before proposing refactors or API changes.
  • Multi-agent fleet coordination: A shared ledger keeps Letta, Hermes, and custom bots working from the same verified facts.

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Next steps