Hire digital workers.
Supervise them
like employees.
Bring any agent. Govern every action. AgentOS puts AI workers into your business to do the repetitive work — with the same identity, least-privilege access, approvals and audit trail you'd demand of a real hire. On the models, cloud and identity you already run.
Building an agent is the easy part. Running a hundred of them is the business you're actually in.
Every board said "have an AI strategy"
Almost no executive was told the hard part. Frontier models are abundant, cheap and improving monthly — intelligence is not the constraint.
You cannot let software act unsupervised
Not inside a regulated business. Not without knowing who did what, on whose authority, against which data — and with what proof afterward. The barrier is trust, not capability.
Pilots stall at the security review
A demo becomes a deployment only when someone can answer the audit question. Most teams rebuild that answer by hand, per agent, forever.
For enterprises that must adopt AI agents without losing control, AgentOS is an Enterprise AI Operating System that governs autonomous agents as a digital workforce — giving every agent an identity, enforcing least-privilege access and human approvals on every consequential action, orchestrating long-running work durably, and recording a complete audit trail — on the models, clouds and identity systems you already run.
Eight things a framework will never do for you.
Explore the platformAgent Identity
Every agent is a named principal.
Delegated Authority
Acts on authority that travels with the request.
Policy Enforcement
Re-derived at request time, enforced at the resource.
Human-in-the-Loop Approvals
Durable sign-off that survives restarts.
Budget & Cost Governance
Quotas and envelopes the ledger actually enforces.
Audit & Evidence
Correlated evidence for every action.
Agentic Mesh
Authorization-bound agent-to-agent calls.
Governed Connectors
Your knowledge, your entitlements, on the way out.
Three moves. None of them asks you to start over.
Watch a governed actionBuild in your framework
LangGraph, the OpenAI Agents SDK, whatever your engineers already chose. We never compete on authoring ergonomics — we govern whatever they produce.
agent = LangGraph(...) # unchanged Govern with AgentOS
A thin SDK over open contracts gives the agent an identity, scoped credentials, delegated authority and an audit trail. It stays a lean standalone service.
from agentos import principal, policy Run on your model
Model is a configuration value. Cloud is any Kubernetes. Identity federates to your IdP. Leave whenever you like — that is the point.
LLM_PROVIDER=bedrock # no rebuild Humans and agents sit in one directory, at par.
An agent is a first-class security principal — a digital worker, not a script. That framing isn't marketing gloss; it's the literal data model.
| Member | Kind | Access | Status | |
|---|---|---|---|---|
| Alex Reed Controller · Finance |
Person | ledger.approve | Active | |
| Mira Okonkwo Head of Risk |
Person | policy.author | Active | |
| Dan Whitfield Platform Engineer |
Person | skills.publish | Active | |
| finance-recon Reconciliation · L2 |
Agent | ledger.read/write | Running | |
| claims-triage Intake · L1 |
Agent | phi.read (row ABAC) | Running | |
| sdlc-builder Software factory · L3 |
Agent | repo.write | deploy | Approval |
| contract-reader Legal ops · L1 |
Agent | docs.read (scoped) | Idle |
procurement-agent
Raise POs under $25k, escalate above.
support-triage
Route and draft tier-1 replies, never send unsupervised.
recruiting-screener
Screen applications against the rubric only.
audit-evidence-agent
Assemble evidence packs from the ledger on request.
Autonomous at the reasoning layer. Deterministic at the orchestration layer.
Agents should be free to think — to interpret ambiguous requests, plan, choose approaches. They should not be free to act without control. Every action walks the same recordable path.
An agent is a principal
Identity, credentials, least-privilege access, delegated authority — everything attributable. Not marketing gloss; the literal data model.
Governance is the product
Model quality is a fast-moving external input. Safely delegating authority and proving it afterward is the durable value.
Decide from the store
Authentication verifies at the edge. Authorization is re-derived at request time and enforced at the resource — never the UI.
Fail closed
Policy engine unreachable? Provenance ambiguous? Token malformed? The answer is no. Every design doc defaults to denial.
What your CIO says at 9am. What we answer by 9:05.
Every objection below came out of a real buyer conversation. The answer is a layer of the platform, not a promise.
AgentOS is the control layer that makes agents adoptable: every worker only touches what you allow, consequential actions need human sign-off, everything is recorded. Every other objection is a facet of this one.
Every action, delegation, policy decision and hand-off emits a structured, correlated audit event — reconstructable even across thousands of concurrent agents.
Authorization is re-derived on every call and enforced at the resource server, so learning a URL or forging a prompt grants nothing. Agents reach only the agents the reachability graph permits.
Human-in-the-loop approval is a durable platform primitive: the agent proposes, a human approves, and the workflow survives restarts while it waits — minutes or days.
We federate to your IdP over OIDC; humans log in through existing SSO. Authorization is always re-derived on our side, so stale token claims can never widen access.
A unified egress gate replays the requester’s entitlement against the provenance of what is being returned — closing the confused-deputy gap where an agent reads broadly but answers a narrowly-entitled caller.
One agent is a demo. A hundred is an organization.
You can't climb from Stage 1 to Stage 3 on a framework — a framework has nothing to say about authority.
A human chats with a model
No governance problem yet
Value is real but bounded. The human does all the acting, so nothing autonomous touches a system of record.
AgentOS’ job here: Nothing to govern. Most enterprises are already past this.
Chat interface · Human executes · No system access · No audit need
Software that decides and acts
The first governance problem
The agent can now touch systems. Most enterprises are here, nervously — a pilot in production and a security team asking hard questions.
AgentOS’ job here: Give the worker an identity, scope its credentials, record what it does.
Agent identity · Least-privilege creds · Action audit · Tool allow-lists
An orchestrator plus specialists
Where frameworks stop
Agents call each other and the questions multiply. Can the orchestrator reach an agent the user can’t? Who is accountable for a hand-off?
AgentOS’ job here: Agentic mesh with per-hop delegation and a reachability graph.
Per-hop delegation · Reachability graph · Hand-off attribution · A2A transport
Hundreds of workers, supervised like an org chart
What AgentOS is built for today
Each has a role, least-privilege access and an audit trail across departments — humans and agents at par in one Members directory.
AgentOS’ job here: Unified directory, tenant-scoped roles, skills governance, durable approvals, full audit.
Members directory · Tenant-scoped roles · Skills governance · Durable approvals
Hired, assigned, measured, retired
Seeded today
Executives manage a blended human-and-digital workforce from one console — cost, latency and utilization per worker.
AgentOS’ job here: Executive dashboards and per-worker cost attribution are the seeds of this.
Cost per worker · Utilization view · Budget envelopes · Retire & rehire
AI stops being a project
The endgame
The enterprise runs a governed internal software factory and service factory, continuously. NirvanaLab is the concrete proof-of-concept.
AgentOS’ job here: Provide the identity, authority, orchestration and accountability that make the next rung safe to attempt.
Autonomous SDLC · Continuous service factory · Board-level reporting · Regulatory posture
Use their models. Govern them with us. Take your agents with you.
Lock-in is a liability, not an asset. Paradoxically, making it easy to leave is what makes enterprises comfortable adopting.
OpenAI · Bedrock · Vertex · Azure · Self-hosted
Model is a configuration value, not an architecture decision.
LLM_PROVIDER=openai # no rebuild EKS · AKS · GKE · On-prem
If it runs Kubernetes, it runs AgentOS.
helm install agentos → EKS Okta · Entra · Keycloak
Authorization is re-derived on our side, every call.
OIDC federation → Okta The same governance core, five very different rooms.
Enterprise automation
Judgement-heavy back-office work, done under a scope and a ceiling.
Software engineering
Agents that design, build and deploy — with every commit attributable.
Operations & incidents
On-call autonomy inside a blast radius you set in advance.
Healthcare
Regulated workflows where guardrails are enforced, not suggested.
See AgentOS govern a real action, end to end.
Forty-five minutes with an architect, on a live cluster. No deck, no sandbox theatre.