AI control plane
Also called: agent control plane, agentic AI control plane
The shared layer that composes, authorizes, routes, and records agent work, separate from the applications built on top of it.
What ai control plane means.
An AI control plane is the infrastructure layer that governs how agents run, as distinct from the applications that use them. The term is borrowed from networking, where a control plane makes decisions and a data plane carries traffic. Applied to agentic AI, the control plane holds workflow composition, policy enforcement, model routing, tool authorization, human approval, observability, and audit, so that each application inherits those behaviors rather than reimplementing them. The practical test of whether something is a control plane is substitution: if a product on top can be replaced without reimplementing governance, the governance lives in the control plane.
Why it matters.
Governance implemented per application drifts. Ten teams write ten audit formats and ten interpretations of what needs approval, and no reviewer can reason across them. Centralizing those decisions means a policy change applies everywhere at once, and an auditor learns one record format. It is also what makes a portfolio of vertical products economically viable: the expensive, security-critical layer is built once.
How it works in practice.
Workflow composition
Agent teams are declared as governed missions rather than assembled ad hoc in application code, so the same run is repeatable and reviewable.
Model routing
Each task is directed to an appropriate model, hosted or on-premise, so provider choice is a routing decision rather than an architectural commitment.
Policy and authorization
One decision point evaluates access and tool permission for every agent in every application.
Observability and audit
One record format across every product, which is what makes cross-application review and accreditation tractable.
What people get wrong.
That an agent framework and a control plane are the same category. A framework is a library you assemble into an application, and each application ends up with its own governance posture. A control plane is a running system that enforces governance for every application on it, whether or not the application's authors thought about it.
Common questions.
Is a control plane the same as an agent framework?
No. A framework gives you building blocks and leaves policy, approval, and audit as your responsibility. A control plane enforces them centrally. Frameworks are excellent for constructing agents; the control plane is what makes running them in a regulated environment defensible.
Can I run a control plane on-premise?
A control plane can run on-premise or in a disconnected environment. Trunnion deployment options and control behavior are verified for the specific release, configuration, and customer boundary.
Does Trunnion license its control plane on its own?
Trunnion offers portfolio products and scoped builds rather than a standalone control-plane license. Internal platform naming and implementation details are withheld pending intellectual-property clearance.
Standards referenced.
Trunnion is engineered to align with these control families. Alignment by design is not a claim of current certification or authorization.
Read next.
- Multi-agent orchestrationCoordinating several specialized AI agents through a defined workflow, with explicit handoffs, state, and control over the sequence.
- LLM-agnostic model routingDirecting each task to an appropriate language model at runtime, so a provider can be changed without rewriting workflows.
- Agent governanceThe control layer that decides what an AI agent may do, who approves consequential actions, and how every step is proven afterward.
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See ai control plane running on your workflow.
Governance, approval, and provable audit built into the control plane, from cloud to air-gap. Request a demo against a workflow you actually run.