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Whitepaper · Enterprise AICP

The Enterprise AI Control Plane

A practical guide to governing agents, models, tools, and workflows before they reach production.

A reference architecture for governed production AI
Identity, policy, approvals, observability, and cost control
A practical path from one workflow to enterprise scale
Cover of The Enterprise AI Control Plane whitepaper
Balaji Nagaraj KumarWritten byBalaji Nagaraj KumarMoring AI · Published 11 September 2026 · 8 pages · 16 min read
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What you will learn

Govern production AI as one operating system.

The paper explains the control plane decisions that sit between an AI pilot and a production system: who can act, which tools can be used, what must be reviewed, and how every decision is traced.

Control

Govern before development scales

Put identity, policy, approval, and data boundaries around AI work before agents reach sensitive tools and systems.

Operation

See every model, tool, and agent

Create a shared view of model access, agent activity, cost, traces, incidents, and human checkpoints.

Scale

Move from one workflow to many

Reuse controls and approved building blocks while adapting the control plane to each customer environment.

Inside the paper

A practical architecture, not a category pitch.

See how the control plane connects enterprise identity, model gateways, approved tools, agent guardrails, human decisions, and operational evidence.

Page 01 — The scaling wall: why enterprise AI stalls before production
Page 02 — The control plane, and how teams build AI in 2026
Who should read it

For leaders responsible for AI in production.

Use the paper to align technology, security, governance, and delivery teams around one practical operating model.

01

CIO, CTO, and CDO

Define how AI moves from approved experimentation into governed production.

02

AI and platform engineering leaders

Standardize models, tools, agents, observability, and deployment controls.

03

Security, risk, and governance teams

Make policy, approvals, evidence, and accountability part of the runtime.

About the author
Balaji Nagaraj Kumar
Balaji Nagaraj Kumar
VP of Engineering, moring

This paper brings together the architectural and operating decisions enterprises need to make before production agents can be trusted across teams, tools, and environments.

Frequently asked questions

Before you download.

A short explanation of the category, the paper, and how moring approaches control-plane delivery.

What is an AI Control Plane?+
An AI Control Plane is the operating layer used to govern models, tools, agents, identity, policy, approvals, observability, cost, and audit evidence across production AI workflows.
How does AICP relate to the customer's control plane?+
AICP is the reusable engine moring uses to assemble a customer-specific AI Control Plane. The final implementation is shaped around the customer's workflows, environment, controls, and operating model.
Will downloading the paper trigger a sales follow-up?+
No. The paper is delivered using your work email. Research and product updates are optional, and a sales conversation starts only when you ask for one.
The Enterprise AI Control Plane

Put governance in place before production AI scales.

Read the architecture, operating model, and adoption approach in one concise paper.

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