AI agent governance is how you stay accountable for the AI your company runs: who owns each agent, what it's allowed to read and write, what it costs, and whether it stays inside policy. Fleece turns the live knowledge graph of every agent, tool and person into a control plane — so oversight, access control and audit aren't a spreadsheet you chase quarterly, but a property of the system itself.
Ungoverned AI sprawl is a liability.
You can already see your agents. The harder question is who is responsible when one of them ships a wrong answer, leaks a document, or quietly spends ten times its budget over a weekend.
Without ownership, access rules and an audit trail, every agent is a blind spot — and as the count grows, so does the surface for cost overruns, data exposure and compliance failures no one signed off on.
- +No clear owner means no one accountable when an agent misbehaves
- +Open read/write access turns one prompt into a data-exposure path
- +No audit trail means no answer to "who changed this, and when?"
The live map becomes a control plane.
Fleece already maps every agent, tool and person onto one graph. Governance is the layer that makes that map enforceable: each node carries an owner, an access policy and a budget, and the brain checks them on every read and write.
Instead of governing AI in a side document that's stale the day you write it, you govern it where the work happens — on the same live graph your agents already think with.
Who can read, who can write, who owns it.
Role-based access control (RBAC) decides what each person and agent can see and change in the shared brain — sensitive collections stay scoped, and SSO through Google or Microsoft ties every action to a real identity.
Every agent has a named owner and a live cost line, so accountability is never ambiguous. When something goes wrong, you know who's responsible and exactly what happened.
Built for oversight and audit.
An append-only audit log records who accessed what, when and through which client — the evidence trail reviewers and frameworks like the EU AI Act and SOC 2 expect, without a separate logging stack.
Because the brain is local-first plain markdown on your own infrastructure, data residency is a setting you control, and optional cloud sync stays end-to-end encrypted with AES-256-GCM.
What AI agent governance gives you.
Owner on every agent
Each agent, tool and integration carries a named owner — so accountability is explicit and nothing runs unattended.
Role-based access control
RBAC scopes what each person and agent can read or write. Sensitive collections stay locked to the roles that need them.
Audit log of every action
An append-only trail of who accessed what, when and through which client — the evidence reviewers ask for.
Cost budgets per agent
Live model, token spend and run-rate per agent, against a budget — so a runaway agent is caught, not discovered on the invoice.
SSO identity
Single sign-on via Google or Microsoft ties every read and write to a real person, not a shared key.
Policy you control
Local-first markdown on your infrastructure means data residency, retention and encryption are settings you own — built for compliance.
Questions about AI agent governance.
What is AI agent governance?+
It's the practice of staying accountable for the AI agents your company runs — defining who owns each one, what it's allowed to access, what it costs, and whether it stays inside policy. Fleece enforces this on the live graph of your agents, so oversight, access control and audit are properties of the system rather than a quarterly spreadsheet.
How does Fleece control what agents can access?+
Role-based access control (RBAC) scopes every read and write to the shared brain, and SSO through Google or Microsoft ties each action to a real identity. Sensitive collections stay locked to the roles that need them, so one prompt can't quietly become a data-exposure path.
Is Fleece SOC 2 or EU AI Act certified?+
Fleece is built for that kind of oversight rather than claiming a certification: an append-only audit log, RBAC, SSO, local-first data residency and end-to-end encryption give you the evidence and controls those frameworks expect. You keep the data on your own infrastructure, which makes your compliance posture yours to attest.
How do we control AI agent costs?+
Every agent exposes its live model, token spend and run-rate, and you can hold each against a budget. Because it's continuous rather than a monthly surprise, a runaway agent is caught the moment its spend climbs — not after the invoice arrives.
Where does the audit and access data live?+
On your own infrastructure, as local-first markdown — the audit log and access policy travel with the brain, not a third-party SaaS. Optional cloud sync is opt-in and end-to-end encrypted with AES-256-GCM, so data residency stays a setting you control.
See every agent. Govern every agent.
Turn the live map of your AI into a control plane — owners, access, audit and budgets on one graph. Start a 14-day trial, no card required.