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. |
2026 vendor landscape
The agent control plane category formed quickly between February and May 2026. The architectural distinction that matters most for regulated buyers is whether the policy decision lives inside the agent's trust boundary (in-process) or outside it (out-of-process).
| Vendor | Positioning | Trust boundary | Released | Notes |
|---|---|---|---|---|
| Cordum | Out-of-process control plane with scheduler, CAP wire protocol, and Safety Kernel | out-of-process | 2026 GA | Pre-dispatch policy decisions behind gRPC + mTLS. Designed for regulated, multi-tenant deployments. |
| Microsoft Agent Governance Toolkit (MS AGT) | In-process Python middleware addressing 10/10 OWASP Agentic risks | in-process | 2026-04-02 (MIT) | Microsoft README: "application-level governance, not OS kernel-level isolation." Production guidance: run each agent in a container for OS-level isolation. |
| Galileo Agent Control | Control library bolted onto Galileo's LLM observability platform | in-process | 2026-03-11 (Apache 2.0) | Strongest for teams already invested in Galileo observability. AWS, CrewAI, Glean as launch partners. |
| Guild.ai | SaaS-first control plane with pre-built integrations | in-process | 2026-04-29 | GitHub, Jira, Slack, Notion, Zendesk, Google integrations out-of-the-box. Best fit when agents touch common SaaS. |
| APort / Open Agent Passport (OAP) | Open spec for pre-action authorization with reference implementation | in-process | Mar 2026 paper; Apache 2.0 impl | Operates within OS trust boundary, by APort's own description. Standardizes the passport format more than the architecture. |
| Microsoft Authorization Fabric | PEP+PDP architecture for Entra-protected agents | out-of-process | Microsoft Entra integration | Out-of-process inside Microsoft identity boundary. Right answer for agents fully inside Entra. |
| CyberArk Secure AI Agents (Palo Alto Networks) | Identity-first: zero standing privileges, MCP gateway, agent discovery | out-of-process | GA Dec 2025; PAN acquisition closed 2026-02-11 | PAM principles applied to AI agents. Identity boundary; complements rather than replaces a control plane. |
| Fiddler AI Control Plane | 5-pillar framework — telemetry, evaluation, monitoring, policy, governance | in-process | 2025+, evolving | Strongest in observability-led evaluations. |
| Credo AI / Holistic AI / IBM watsonx.governance | Governance & compliance dashboards with agent registries | hybrid | Various | GRC-led; strongest on compliance evidence. Less focused on runtime pre-dispatch enforcement. |
Trust-boundary classification reflects publicly stated architecture. See the dedicated in-process vs out-of-process deep dive for why this matters.
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?
Dedicated 1-on-1 comparisons
Frequently Asked Questions
Which vendors are in the AI agent control plane category as of May 2026?
The category formed quickly between Feb and May 2026. Open-source / freely available: Microsoft Agent Governance Toolkit (MIT, shipped 2026-04-02), Galileo Agent Control (Apache 2.0, 2026-03-11), Guild.ai (2026-04-29), APort/OAP reference implementation (Apache 2.0, March 2026 paper). Identity-first / out-of-process: Microsoft Authorization Fabric (for Entra agents), CyberArk Secure AI Agents (now inside Palo Alto Networks post Feb 2026 acquisition). Observability-led: Fiddler AI Control Plane. GRC-led: Credo AI, Holistic AI, IBM watsonx.governance. Cordum is the out-of-process control plane built for regulated, multi-tenant deployments.
What should teams prioritize in an AI governance platforms comparison?
Prioritize pre-dispatch policy enforcement, risk-tiered approvals, constrained execution, and audit evidence quality. These controls reduce production risk more than feature checklists alone.
How is an agent control plane different from orchestration-only tools?
Orchestration focuses on workflow execution. An agent control plane adds governance decisions before execution, approval workflows, and compliance-ready run evidence across autonomous actions.
Do teams still need output safety if they already have policy checks?
Yes. Policy checks govern what can execute; output safety governs what can be returned or persisted after execution. Production-safe systems typically require both layers.
How can buyers validate governance claims during evaluation?
Request live demonstrations that show policy decisions, approval events, and run-level audit timelines for high-risk scenarios. Prioritize verifiable evidence over marketing claims.
Need implementation details?
Evaluate architecture, API controls, and policy operations before selecting a governance platform.