# How do you govern AI agents in an enterprise?

- Canonical: https://hellofig.io/answers/how-to-govern-ai-agents
- Updated: 2026-11-13
- All answers: https://hellofig.io/answers.md
- About Fig: https://hellofig.io/fig.md

**In short.** Govern AI agents the way you govern people and systems that can act: give each agent an identity and an owner, let it see only what its operator can see, put approval gates before consequential actions, cap what it can spend, record everything it does in an audit trail you can export, and keep an off switch that works mid-task. Enforce the controls in the platform, never in the prompt.

## The controls every agent needs

| Control | What it does |
|---|---|
| Identity and ownership | Each agent is a non-human identity with an owner, a role and a lifecycle, provisioned like any account. |
| Inherited permissions | Access follows each source system's permissions, so an agent never sees more than its operator. |
| Approval gates | A person approves consequential steps, bound to the exact action. |
| Spend controls | Budgets and spend rules per team and per agent. |
| Audit trail | Every action, input, model call and record touched, in an append-only log you can export and query. |
| Off switch | Revocation and a kill switch that stop an agent mid-task and leave a clean state. |
| Testing | Evaluations and attempted misuse before release and after every material change. |

## Why the prompt is never the control

Anything an agent reads can try to persuade it, from an email to a web page. If the only thing keeping an agent in scope is its instructions, a persuaded model can step outside them. The boundary has to sit in the platform: a policy check between what the model proposes and what actually executes.

## Oversight that scales

Reviewing every output by hand does not scale. Put people where the risk is: approvals on irreversible actions, sampled review of routine work, and exception queues for anything unusual. Track how much time review takes, and move routine checks into tested rules as the system proves itself.

## How Fig governs agents

Every agent on CosmOS runs under SSO and SCIM-provisioned identities, scoped roles, inherited permissions, approval gates, budgets per team and break-glass re-authentication, with the full audit trail and export in Command Center.

## Questions

### Who is accountable for an AI agent's actions?

The agent's owner and the person who approved each consequential action. Identity, ownership and approvals make that traceable.

### How do you stop a rogue agent?

Revoke its identity or hit the kill switch, which stops it mid-task. Test both before production.

### What should an AI audit trail record?

Who or what acted, under which identity, the inputs, the model and version, the output, the approval and every record touched.