AI CONTROL plane
A practical guide to governing agents, models, tools, and workflows before they reach production.
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WHAT YOU WILL LEARN
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
Put identity, policy, approval, and data boundaries around AI work before agents reach sensitive tools and systems.
OPERATION
Create a shared view of model access, agent activity, cost, traces, incidents, and human checkpoints.
SCALE
Reuse controls and approved building blocks while adapting the control plane to each customer environment.
INSIDE THE PAPER
See how the control plane connects enterprise identity, model gateways, approved
tools, agent guardrails, human decisions, and operational evidence.


WHO SHOULD READ IT
Use the paper to align technology, security, governance, and delivery teams around one
practical operating model.
01
Define how AI moves from approved experimentation into governed production
02
Standardize models, tools, agents, observability, and deployment controls
03
Make policy, approvals, evidence, and accountability part of the runtime.
ABOUT THE AUTHOR
Balaji Nagaraj Kumar
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
A short explanation of the category, the paper, and how moring approaches control-
plane delivery

THE ENTERPRISE AI CONTROL PLANE
Read the architecture, operating model, and adoption approach in one concise
paper