AI AGENT CONTROL PLANE & ENTERPRISE AI GATEWAY
AgenixCore
AgenixCore is AgenixHub's AI agent control plane: a governed layer that sits between your people, agents, and every model, tool, and data source. It governs access, routes requests to the right model, controls spend, and captures every interaction — turning scattered AI adoption into one accountable operating system.
Policy-first by design
Secure. Compliant. Governed.
Cost-efficient by default
Optimize spend and reduce waste.
Built for scale
Enterprise-grade reliability.
Employees
Applications
AI Agents
APIs & Services
Access Governance
Verify identity and enforce least privilege
Model Routing
Select the right model and path every time
Secure Context
Assemble safe context from trusted sources
Cost Controls
Prevent waste and optimize every request
Audit Logs
Capture, store, and make auditable
Access Governance
Verify identity and enforce least privilege
Model Routing
Select the right model and path every time
Secure Context
Assemble safe context from trusted sources
Cost Controls
Prevent waste and optimize every request
Audit Logs
Capture, store, and make auditable
CONTROL PLANE FOUNDATION
Employees
Applications
AI Agents
APIs & Services
Access Governance
Verify identity and enforce least privilege
Model Routing
Select the right model and path every time
Secure Context
Assemble safe context from trusted sources
Cost Controls
Prevent waste and optimize every request
Audit Logs
Capture, store, and make auditable
Access Governance
Verify identity and enforce least privilege
Model Routing
Select the right model and path every time
Secure Context
Assemble safe context from trusted sources
Cost Controls
Prevent waste and optimize every request
Audit Logs
Capture, store, and make auditable
CONTROL PLANE FOUNDATION
How do you start building an AI agent control plane?
Start by mapping where AI usage creates value, waste, and risk. The AgenixCore methodology then helps discover, assess, and optimize AI usage across your enterprise continuously.
Audit
Discover current usage, flows, and risks.
Map where AI is used across people, apps, agents, data, and model paths.
Deploy
Apply policy, routing, and guardrails.
Introduce the control layer around the highest-value, highest-risk paths first.
Operate
Continuously optimize cost, control, and adoption.
Keep usage observable while teams expand AI workflows inside clear rules.
Executive control model
What is an AI agent control plane?
It is the governed layer that turns scattered AI adoption into one operating system — deciding who is using AI, what policy applies, where requests route, and what leadership can review.
Who is using AI, from which workflow, and for what business purpose?
Which requests should be approved, blocked, routed, limited, or reviewed?
Which model, context source, or data path is appropriate for the task?
What must be visible to finance, security, operations, and leadership?
Governance layers
How does an AI control plane govern requests before they reach a model?
Control is applied before AI requests reach sensitive destinations: users, apps, and agents enter one governed layer before models, search, private systems, or enterprise data are touched.
Identity and role controls
Policy and authorization
Model and tool routing
Spend and usage limits
Approved context access
Audit and operational visibility
AI agent control plane, in depth
Read more on the AI agent control plane and enterprise AI governance
Architecture, comparisons, ROI measurement, and how the EU AI Act intersects with AI agent control plane governance.
What Is an AI Control Plane?
The core architecture: how a control plane governs people, apps, agents, models, tools, and data.
AI Control Plane vs. LLM Gateway
Where a gateway ends and a control plane begins — and why enterprise AI often needs both.
Control Plane for AI Agents: A Reference Architecture
A six-layer architecture for running AI agents in production with routing, governance, and observability.
AI Governance vs. AI Control Plane
Governance defines what should happen. A control plane helps operationalize how it happens.
ROI of an AI Control Plane
The operational metrics that show whether a control plane is actually delivering value.
EU AI Act 2026: What Actually Changes in August?
What's live from 2 August 2026, what moved to 2027–2028, and why the confusion happened.
Provider vs Deployer Under the EU AI Act
Using ChatGPT or Claude doesn't usually make you a provider. Here's how to tell which role applies.
Do You Need EU AI Act Logging?
What logging the law actually requires, and what's just good governance practice.
EU AI Act Transparency Rules for Manufacturing AI
How Article 50 applies to manufacturing AI deployments, with three practical scenarios.
AgenixCore and the EU AI Act
How AgenixCore's governance features map to Article 50 — and what it isn't a substitute for.
The Enterprise Guide to the EU AI Act (2026–2028)
The full reference: timeline, provider vs. deployer, Article 50, high-risk systems, and a governance operating model.
Audit > Deploy > Operate
Build a governed AI operating layer before adoption spreads further.
Start with the AI Operating Efficiency Audit, then deploy AgenixCore around the workflows where cost, risk, and operational visibility matter most.