AI Company OS
The operating layer we run MAS-AI on: several AI coding agents sharing one memory, computed routing and deterministic checks.
The problem
Using several AI assistants at once creates the same problems as a team with no shared notes: duplicated work, lost decisions and nobody checking the result.
What we engineered
Treat agents like colleagues. Give them one append-only record of decisions, route each task by measured availability instead of memory, and let deterministic checks, not the model, decide when work is done.
- A router that probes each model lane for real and sends work to the cheapest lane that can do it well.
- Verifiers that return held, breached or inconclusive, so a task is done when a check says so.
How it connected
- An append-only shared memory log that every agent reads at the start of a session and writes at the end.
- Handoff packets that carry the problem, evidence and proposal between agents, never raw transcripts.
What technology was appropriate
Python tooling, hooks into each assistant, scheduled jobs and ledgers for every verdict.
Where it stands
Internal, used every day. It is how a founder-led company runs research, engineering and review in parallel. This website was planned through it: two independent AI reviewers critiqued the plan before any code was written.
Current state, stated as it is today.
Automate operations
Workflow automation, AI agents and knowledge assistants that take repeated work off your team.
Security, evaluation and governance
Security reviews, evaluation, audit trails and human approval where an action matters.
Bring us the bottleneck.
If your problem looks like one of these, tell us. We will say whether a build is the right answer.
Bring us a bottleneck