Context

The New Stack article argues agentic AI needs a dedicated "control plane" -- a layer that inventories agents, enforces behavioral policies at runtime, provides token metering/cost tracking, and delivers end-to-end observability.

Why This Matters

SynthOrg IS effectively building this control plane (HR = agent inventory, security = policy enforcement, budget = token metering, observability = telemetry). But we don't frame it as one, and may have API surface gaps. The framing sharpens positioning and reveals whether external consumers can query the right primitives.

Action Items

• Map SynthOrg modules to control-plane primitives: agent inventory, runtime policy enforcement, token metering, telemetry
• Identify gaps: is agent inventory queryable from the API? Are policies expressible as code? Is telemetry exportable?
• Validate that "write policies once, enforce across all deployments" works with our rule engine + autonomy levels
• Consider positioning implications for docs/landing page

References

• The New Stack: Agentic AI Control Plane
• References Galileo Agent Control (open source) as example implementation