We build context infra that helps AI coordinate knowledge.

Context is what makes LLMs useful. Yet context is never complete. Knowledge lives across people, teams, databases, repos, workflows, APIs. Local, fragmented, often contradictory. It gets stale as soon as the world moves.

How to solve it? First we tried duct-taping systems together. Connect every source, pipe everything into one place. Well, this failed spectacularly. Systems got noisy, and change arrived faster than they could adapt. We rediscovered an old truth: knowledge is never centralizedHayek was arguing against central planning.“The knowledge of the circumstances of which we must make use never exists in concentrated or integrated form.”F. A. HayekEconomist“The Use of Knowledge in Society”.

Agents helped us bridge this gap. Embedded into the actual circuits of knowledge work, they are capable of discovering context: traversing systems, getting real-time data, reading logs, tracing dependencies. They can synthesize and make decisions, producing real changes.

The problem is: each agent lives in its own reality. More agents produce more versions of reality. They lose coherence: their writes collide, their world models drift.

To solve this problem, agents need coordination and error correctionDeutsch is generalizing from how science works.“Without error-correction all information processing, and hence all knowledge-creation, is necessarily bounded. Error-correction is the beginning of infinity.”David DeutschPhysicistThe Beginning of Infinity. Not as sugar on top, but as the operating principle. Truth is never static: it is not a set of claims, but the process of reconciliation. AI needs a shared graph and a set of protocols to maintain an evolving world model: ontologies, consensus state, branches, provenance, criticism, failed attempts, and outcomes.

That is what we are building.