Comparison
AI Governance Platforms Comparison
Choose a platform by governance depth: policy enforcement, approvals, auditability, and production reliability.
Most evaluations over-index on orchestration features and under-index on governance. If your target state includes autonomous AI agents in production, governance should be a primary decision axis.
This comparison uses architecture categories to help security, platform, and engineering teams align on what they actually need.
| Criterion | Agent Control Plane | Orchestration-First | Framework-First | Sandbox-First |
|---|---|---|---|---|
| Pre-dispatch policy enforcement | Native and centralized across jobs and workflow steps. | Usually implemented in app logic or middleware. | Typically delegated to user-defined code patterns. | Often focused on isolation, with limited policy semantics. |
| Human approval workflow | Built-in approval outcomes tied to risk and policy context. | Possible through custom step patterns and signals. | Manual implementation, often inconsistent across teams. | May provide manual checkpoints, but workflow context can be shallow. |
| Deterministic constraints | First-class allow-with-constraints path. | Usually custom logic inside activities or workers. | Depends on agent implementation details. | Runtime restrictions may exist but policy granularity varies. |
| Audit evidence quality | Run timelines plus policy and approval evidence. | Strong execution history, weaker policy evidence by default. | Logging quality depends on app code discipline. | Execution logs are often available; policy causality may be limited. |
| Operational reliability controls | Routing, retries, timeout handling, DLQ, reconciliation. | Strong reliability primitives for workflow execution. | Varies by integration and runtime choices. | Isolation is strong; workflow resiliency varies by architecture. |
Questions to ask every vendor
- Can your platform explain every policy decision made before execution?
- Can your team enforce approval workflow rules consistently across all agent projects?
- Can your audit system reconstruct who approved what, and under which policy snapshot?
- Can you constrain risky actions instead of only allowing or denying them?
- Can you scale controls across multiple autonomous AI agents without rewriting app logic?
Related pages
Need implementation details?
Evaluate architecture, API controls, and policy operations before selecting a governance platform.