8 Best AI Agent Governance Platforms in 2026
Governing an AI model and governing an AI agent are different problems. A model answers; an agent acts. Once agents can read customer records, change orders and call APIs, governance means controlling what each agent can touch, requiring approval for risky actions and keeping a record you can audit. Some tools build that into the place agents run; others watch agents from the outside.
Below are eight AI agent governance platforms, grouped by approach, with the point where each one stops. For governance of AI models and policies more broadly, see the best AI governance solutions for enterprises.
What agent governance needs to cover
- An inventory of agents: who built them, what they connect to.
- Least-privilege access: agents see only the data their role allows.
- Human approval for actions that move money, delete data or contact customers.
- Logs of every read and write, tied to a user or agent.
- Policy and risk documentation for regulators and auditors.
The eight platforms at a glance
| Platform | Approach | Best for |
|---|---|---|
| Jet Admin | Governance built into where agents run | Agents on your own business data |
| Microsoft Purview | Data governance for Copilot and agents | Microsoft 365 organizations |
| ServiceNow AI Control Tower | Central AI inventory and oversight | ServiceNow customers |
| Salesforce Einstein Trust Layer | Guardrails for Agentforce | Salesforce customers |
| IBM watsonx.governance | AI lifecycle risk and compliance | Regulated enterprises |
| Credo AI | AI policy and risk management | Governance and compliance teams |
| Holistic AI | AI risk audits and monitoring | Risk and compliance teams |
| Zenity | Security for low-code and AI agents | Security teams |
Governance built into the platform
1. Jet Admin
Jet Admin governs agents where they run. Agents built in Jet act on your databases and APIs through the same roles and row-level permissions as the app's users, so an agent cannot read or change more than its role allows. Sensitive actions can route through an approval step, and activity is recorded in audit logs. Data is queried in place, not copied into another store.
Granular permissions, SSO and audit logs are on the Business plan and above. On-premise deployment and self-hosted AI models are on Enterprise, for teams that need agents and models inside their own network.
Where it stops: it governs agents built in Jet. It is not a company-wide AI inventory or a policy management tool for agents built elsewhere.
2. Microsoft Purview
Purview extends data classification, loss prevention and auditing to Copilot and Copilot Studio agents.
Where it stops: focused on the Microsoft ecosystem.
3. ServiceNow AI Control Tower
AI Control Tower gives a central view of AI assets, risks and approvals across ServiceNow and connected systems.
Where it stops: it assumes ServiceNow as the governance hub.
4. Salesforce Einstein Trust Layer
The Trust Layer adds data masking, zero-retention and audit trails to Agentforce and Einstein features.
Where it stops: it covers AI inside Salesforce only.
Separate risk and compliance tools
5. IBM watsonx.governance
watsonx.governance tracks models and AI applications across their lifecycle, with risk and compliance workflows.
Where it stops: it documents and monitors; it does not control what an agent can access at runtime.
6. Credo AI
Credo AI helps governance teams map AI use cases to policies and regulations such as the EU AI Act.
Where it stops: policy and documentation rather than runtime enforcement.
7. Holistic AI
Holistic AI audits AI systems for risk, bias and compliance.
Where it stops: assessment-focused; controls live in the systems it assesses.
8. Zenity
Zenity discovers and secures agents and apps built on low-code and AI platforms such as Copilot Studio.
Where it stops: a security layer on top of other platforms, not a place to build agents.
Which to use
- You want agents that are governed by design on your own data: Jet Admin.
- Your agents run in Microsoft, ServiceNow or Salesforce: that vendor's governance tools.
- You need AI risk and regulatory documentation: IBM watsonx.governance, Credo AI or Holistic AI.
- Security needs visibility into agents across platforms: Zenity.
Frequently asked questions
What is AI agent governance?
The controls that decide what agents can access and do, who approves risky actions, and how their activity is recorded and reviewed.
Is AI governance the same as agent governance?
Not quite. AI governance often focuses on models, bias and policy. Agent governance adds runtime control over actions and data access.
Do agents need their own permissions?
Yes. The safest pattern is for an agent to act with the permissions of the role it serves, with approvals for anything irreversible.
Govern agents where they work
Start with Jet Admin and build agents with permissions, approvals and audit logs from the start.