The Missing Layer in Your Enterprise AI Stack
You have control planes for networking, identity, and cloud infrastructure. Your AI execution layer doesn't have one — and it's the fastest-growing uncontrolled surface in your enterprise.
The Architecture Problem
AI adoption is outpacing your governance architecture.
Framework Fragmentation
Your enterprise runs LangChain, CrewAI, AutoGen, and custom agent frameworks across different teams. Each has its own execution model, its own governance approach, and its own blind spots. There is no unified governance layer.
Embedded Governance Debt
Governance logic is hardcoded into application code. Every application implements its own policy checks, its own approval flows, its own logging. Updating a compliance rule requires touching N applications across N teams.
No Standardized Decision Schema
Every AI provider returns data in a different format. Every agent framework structures requests differently. Without a standardized schema, you cannot build centralized governance, consistent audit trails, or cross-system analytics.
Retrofit Governance
Audit and compliance requirements arrive after AI systems are deployed. You're being asked to retrofit governance into architectures that were never designed for it — and the result is fragile, inconsistent, and expensive to maintain.
Why This Can't Be Solved at the Application Layer
Application-Embedded Governance
Creates N governance implementations for N applications. Policy updates require coordinated code changes across every team. Policy drift is inevitable. Centralized control is architecturally impossible.
API Gateway Approach
Routes traffic and enforces rate limits. Cannot evaluate the semantic intent of an AI request. Cannot enforce complex, role-based governance policies. Cannot capture decision evidence for audit reconstruction.
Observability-First Approach
Explains what happened after the fact. Cannot prevent an autonomous agent from taking an unauthorized action. Monitoring without enforcement is awareness without control.
Custom Middleware
Works initially. Becomes unmaintainable technical debt at enterprise scale. Every new AI integration requires custom governance code. No standardization, no reusability, no centralized authority.
The Control Plane Architecture
iAgentic provides the architectural layer that decouples governance from execution.
Control Plane
Policy Orchestration
Centralized management for policy authoring, compilation, versioning, and deployment. Policies follow a lifecycle: draft, review, approved, published, retired. Separation of duty enforced — the person who writes policy cannot publish it.
Abstraction Layer
Execution Abstraction
A standardized interface that decouples governance logic from underlying AI infrastructure. Vendor-agnostic. Framework-agnostic. Supports multi-provider format translation and action classification (thinking, acting, reading) for proportional governance.
Enforcement Fabric
Runtime Enforcement
High-performance, distributed enforcement that intercepts AI traffic, evaluates it against compiled policies, and renders deterministic governance decisions. Fail-closed by default. Proportional governance tiers: lite (~5ms), standard, and full (~50ms).
How It Fits Into Your Stack
iAgentic integrates with your existing enterprise infrastructure — not replaces it.
Sidecar Deployment
Deploy alongside existing AI gateways. The Enforcement Fabric operates as an independent governance layer without requiring changes to your existing routing infrastructure.
Inline Enforcement
For direct model access patterns, the Enforcement Fabric sits inline between the application and the AI provider. Every request passes through governance evaluation.
Async HITL Integration
Human-in-the-loop approval workflows integrate with your existing ticketing and approval systems. Stateful orchestration maintains context across the approval lifecycle.
Identity Provider Integration
Native OIDC and SAML support. Connect to your existing enterprise IdP. Identity context flows through every governance decision without requiring custom integration work.
Kubernetes-Native
Runs entirely on Kubernetes. The same manifests deploy in all environments — managed cloud (AWS, GCP, Azure), on-premises, or iAgentic-hosted. No custom infrastructure required.
OpenAI-Compatible API
Agents send requests in standard OpenAI format. No proprietary API, no custom SDK requirement. Drop-in governance for existing AI applications.
Technical Specifications
Decision Latency
<15ms lite, <50ms full (p95)
Throughput
10,000+ decisions/sec per instance
Protocols
HTTP, gRPC, MCP (Model Context Protocol)
Identity
OIDC, SAML — unified human + agent identity
Deployment
Kubernetes-native — same manifests for AWS, GCP, Azure, on-prem
API Format
OpenAI-compatible REST — no proprietary API required
Architect Governed AI Infrastructure
Stop retrofitting governance. Start building it into the architecture.