AgenixCore by AgenixHub
AgenixCore sits between your employees, applications, and agents and your AI models—governing access, routing every request to the right model, connecting secure RAG to internal data, and logging every interaction for audit and cost attribution.
Product definition
AgenixCore is an AI control plane deployed by AgenixHub. It sits between your users, applications, and agents on one side and your AI models and data sources on the other — governing every AI request through role-based access, workload classification, model routing, security policies, token controls, and audit logging.
An AI control plane is a centralized architecture that governs, routes, secures, and audits all AI traffic, requests, and data access across an organization. Instead of having applications connect directly to multiple individual model provider APIs, all traffic flows through the control plane. This enables organizations to apply uniform security policies, role-based access controls, and intelligent model routing across all workflows.
System architecture
AgenixCore structures enterprise AI traffic into three distinct tiers to enforce access controls, security policies, and optimal routing between users and models.
User Layer
Employees use AI through approved interfaces: IDE plugins, custom web applications, desktop assistants, mobile applications, team-specific AI assistants, and internal product APIs.
Supported Interfaces
Control Layer
The core decision engine enforces role-based access, token & usage limits, cost control policies, security standards, approval rules, and attributes logs to department cost centers.
Core Policies
Data Layer
Workloads connect securely to private open-source models, public frontier models, secure RAG context engines, and internal systems like CRMs, databases, and document stores.
Targets & Sources
How it works
Role and department permissions are verified before any request is permitted to proceed.
The request is classified based on complexity as routine, sensitive, knowledge-heavy, or complex.
Security guardrails, token usage limits, feature flags, and approval rules are evaluated and applied.
The request is routed to the most optimal model based on classification, performance goals, and policy rules.
Role-gated context is retrieved dynamically from private databases, CRMs, and document stores when needed.
The complete prompt and response metadata, token count, cost, and user identifier are securely logged.
Model routing
| Workload | Routing target | Optimised for |
|---|---|---|
| Routine / repeatable | Efficient, open-source, or cached models | Cost and speed |
| Sensitive / regulated | Private, VPC, or on-premises models | Privacy and control |
| Knowledge-heavy | RAG + secure retrieval from internal sources | Accuracy and access |
| Complex / high-stakes | Frontier models (OpenAI, Anthropic, Gemini) | Reasoning quality |
Outcomes
Data residency and controlled access. AI runs exactly where your security policies require it—retaining all query logs and model context inside your corporate boundary.
Automatic routing to efficient open-weight models reduces unnecessary spend on high-priced frontier APIs.
Provide secure AI access across all departments, roles, and applications under a unified governance plane.
Granular policies, usage limits, approval workflows, and full audit trail visibility are built in from day one.
Achieve massive cost reduction on routine, repeatable, and knowledge-heavy workloads through intelligent request classification, response caching, and custom model routing.
How AgenixHub implements AgenixCore
AgenixHub provides end-to-end implementation and ongoing operation of the AgenixCore control plane to ensure that your organization remains secure, cost-controlled, and model-optimized.
01
Audit
Identify usage patterns, classify workloads, detect wrong-model usage, and quantify routing opportunities.
Outputs
02
Deploy
Configure the control plane, integrate models and data sources, apply security policies, and wire role-based access.
Outputs
03
Operate
Monitor cost, quality, latency, usage, and policy adherence continuously — adjusting routing and governance as models and workloads evolve.
Outputs
Frequently asked questions
AgenixCore is an enterprise-grade AI control plane and secure AI gateway. It acts as an abstraction and governance layer between your organization's applications, agents, or employees, and various AI models (both frontier and private open-weight models), ensuring security, cost attribution, and policy compliance.
An AI control plane is a centralized architecture that governs, routes, secures, and audits all AI traffic, requests, and data access across an organization. Instead of having applications connect directly to multiple individual model provider APIs, all traffic flows through the control plane, applying uniform security policies and routing logic.
As an enterprise AI gateway, AgenixCore intercepts all incoming AI model requests. It authenticates users and applications, checks department/role permissions, inspects payloads for security violations, routes requests to the most efficient model, connects internal RAG context if needed, and logs the entire exchange for compliance and cost tracking.
AgenixCore is model-agnostic. It routes to public frontier models (such as OpenAI's GPT, Anthropic's Claude, and Google's Gemini), private open-weight models (like Llama, Qwen, or Mistral) running in your VPC or on-premise, and domain-specific fine-tuned models.
Yes. AgenixCore reduces API costs (often by up to 70% on suitable workloads) by classifying incoming requests and automatically routing routine or repeatable tasks to faster, lower-cost open-source models while reserving expensive frontier models only for complex, high-stakes reasoning workloads.
Yes. AgenixCore integrates secure Retrieval-Augmented Generation (RAG) by dynamically pulling context from authorized enterprise data sources (databases, CRM, internal knowledge bases) based on the user's role-based access permissions, ensuring data remains secure and governed.
AgenixCore is typically deployed in your own private cloud, virtual private cloud (VPC), or on-premises environment. AgenixHub manages the end-to-end implementation lifecycle: beginning with an AI Operating Efficiency Audit, followed by configuration/integration deployment, and ongoing managed AI operations.
AgenixHub implements AgenixCore through a structured Audit → Deploy → Operate lifecycle. The engagement begins with an AI Operating Efficiency Audit — mapping your current AI usage, classifying workloads, and identifying routing and governance opportunities.