The operating system for autonomous work.
Most AI systems handle one request. AgentOS operates a governed work system: work orders enter the system, completed outcomes leave it, with authority, evidence, review, recovery, and cost built into every unit of work.
Validated today in software engineering. Designed for governed work everywhere. Multi-agent teams completing real work, attributed by phase, role, model, and token, down to the action.
| Work phase | Primary model | Tokens | Cache reuse | Cost |
|---|---|---|---|---|
| QA | Codex GPT-5.5 | 201M | 92% | ~$227 |
| Development | Claude Sonnet 4.6 | 450M | 97% | ~$210 |
| Orchestration | Claude Opus 4.8 | 208M | 97% | ~$143 |
| Review | Codex GPT-5.5 | 38M | 89% | ~$55 |
| Architecture | Claude Opus 4.8 | 53M | 96% | ~$46 |
| Gatekeeping | Codex GPT-5.5 | 22M | 91% | ~$36 |
| Planning | Claude Opus 4.8 | 26M | 97% | ~$22 |
Answering a question is not the same as completing work. For any piece of completed work, a leader should be able to answer seven questions in seconds, the ones a chatbot can't:
A chatbot returns an answer and hands the work, the proof, and the accountability back to you. AgentOS carries a request all the way to a completed, evidence-backed deliverable.
Produces answers. The work, the proof, and the accountability are left to you.
Produces completed work, with the authority, evidence, and acceptance built in.
Not the conversation, not the agent, not a task board. Authority lives in a durable work order: scope, contracts, and acceptance criteria the work must satisfy to close.
Completion is derived from evidence and independent review, never from an agent's claim that it's done. Nothing closes without proof.
Every change of state, scope, authority, or acceptance is an explicit, recorded transition. The work graph is always in a known, auditable state, never a guess about what happened.
Not "monthly spend." Planning, architecture, development, QA, review, and governance: each attributed, per work order and per deliverable. The cost of work, broken out.
For most systems, conversation lost means state lost. Here, state survives, the runtime is rebuilt, and work resumes from durable state. The hardest problem in autonomous work, solved.
Claude, Codex, OpenAI, local models, the Inference Fabric: interchangeable execution resources. AgentOS owns authority, state, governance, and cost. Providers are workers, not the system.
Workers perform the work. AgentOS determines what work is allowed, how completion is proven, what it cost, and how it recovers.
Authority, grounding, execution, and proof in one flow. Each named system has one job; AgentOS holds them together.
Most agent systems run a loop and hope it converges. AgentOS advances a governed state machine, which is what makes governance, economics, recovery, and completion possible in the first place.
No model, no agent, no session ever holds the state. Authority, progress, evidence, and routing live in a durable work graph outside the model, so when a worker dies, and workers always die, the work doesn't even pause.
Authority and progress live in a durable work graph, not a chat window. Execution picks up exactly where it left off.
A dead Claude, Codex, or session is replaced. Workers are temporary; the work system is permanent.
Crash, kill, or restart, with no operator intervention. Work is recovered, never lost and never duplicated.
Each system has one clear job and one clear boundary. That separation is what keeps it replaceable: swap the memory layer, or run the fabric in front of another swarm, without touching governance. Open any one to dig in.
Authority, evidence, recovery, and economics built into every unit of work. The operating system you are reading about.
You are hereCode, decisions, and references retrieved by meaning, so agents reason from what your organization already knows.
Open FAFO Memory →Specialized AI workers, governed end to end, recovered on failure. The labor that AgentOS coordinates.
Open Agent Swarm →Local and frontier models held at saturation on your NVIDIA GPUs. A single RTX 5090 at 96.9% mean SM utilization.
Open Inference Fabric →Software engineering is where we prove it. But the model, work order, roles, contracts, evidence, review, completion, cost, is about work, not code. The work changes from domain to domain; the governance stays the same, and that is where the market is.
Most platforms provide one of these. AgentOS combines all of them into a single operating system for autonomous work.
AgentOS sits underneath the tools you already run, not against them. Keep Claude Code and Cursor in the editor. Call a frontier agent from inside it. A LangGraph or CrewAI workflow becomes a governed execution contract; a framework persona becomes a governed worker with a scoped tool policy. It adds authority, evidence, and cost, and asks you to rip out nothing.
Governance, a memory layer, fleet-scale inference, GPU vector search, and budget-bounded provisioning each are someone else's whole product elsewhere. Here they arrive as one self-hosted stack, with one cost ledger, one security review, and one runbook. Local and frontier spend land in the same ledger, attributed per task.
The platform, your code, your weights, and the local model tier run inside your perimeter, from a single workstation to a multi-host GPU fleet. Frontier models are optional and governed: AgentOS controls what work is allowed to reach an external model, and attributes every token either way. No hosted source-code custody at any tier.
A single developer on a single machine. Local database, local Git, a small local model, an optional frontier key on the side. Zero cloud dependency by default, ideal for pilots and regulated solo work.
A shared internal runtime for a team or product unit: shared database, shared inference, shared memory, one persona and tool catalog. Where most organizations land for their first production deployment.
A full self-hosted swarm across a multi-host GPU pool, with cross-team dashboards and budget-bounded provisioning across any cloud, your LAN, or your own data center, with zero inbound ports.
We're selecting a small group of design partners to put governed autonomous work into production. If you're putting AI in charge of real work, let's build it together.