Architecture Overview

System Architecture

The iAgentic architecture decouples governance authority from execution logic to ensure enterprise-grade security, deterministic runtime control, and operational agility.

Why Governance Authority Must Be Separated

Embedded governance fails in production for four operational reasons.

1

Policy Drift

Embedded rules diverge across applications. Central updates require N redeployments.

2

Fragmented Enforcement

Application-level checks are bypassed when agents invoke tools directly.

3

Audit Inconsistency

No central authority to reconstruct policy state at the moment of decision.

4

Scaling Problems

Compliance rule changes shouldn't require rebuilding every AI application.

LAYER 1

CONTROL PLANE

Policy Orchestration

Centralized management for policy authoring, compilation, and deployment orchestration. Full policy lifecycle management (draft, review, approved, published, retired) with separation of duty enforcement.

LAYER 2

ABSTRACTION LAYER

Execution Abstraction

A standardized interface that decouples governance logic from underlying model and agent infrastructure. Supports multi-provider format translation and action classification (thinking, acting, reading).

LAYER 3

ENFORCEMENT FABRIC

Runtime Enforcement

A high-performance, distributed layer that intercepts and governs live AI traffic based on compiled policies. Fail-closed by default. Proportional governance tiers: lite, standard, and full.

Control Plane: Policy Orchestration

POLICY AUTHORING

High-level DSL for defining organizational governance intent.

Input:Governance Intent
Output:Policy Models

POLICY COMPILER

Transforms high-level intent into executable logic.

Input:Policy Models
Output:Decision Bundles

DEPLOYMENT ORCHESTRATOR

Manages versioning and rollout of policies.

Input:Decision Bundles
Output:Runtime Updates

DECISION GRAPH ENGINE

Tracks the lifecycle and justification of decisions.

Input:Runtime Logs
Output:Decision Nodes

HITL ORCHESTRATOR

Manages stateful human approval workflows.

Input:Triggered Policies
Output:Resumed Flows

AUDIT & EVIDENCE STORE

Maintains immutable records for compliance.

Input:System Events
Output:Audit Reports

TARGET REGISTRY

Authoritative classification of every tool, API, and system. Each target has data classification, compliance tags, and read/write designation.

Input:Admin Config
Output:Target Catalog

AGENT REGISTRY

Manages agent identities, scoping (allowed actions, targets, max classification), credentials, and per-agent rate limits.

Input:Agent Config
Output:Identity & Scope

Enforcement Fabric: Runtime Enforcement

INGRESS

AI GATEWAY

High-performance entry point for all AI traffic (HTTP/gRPC/MCP).

RAW REQUEST DATA
LATENCY: OPTIMIZED
ANALYSIS

INTENT NORMALIZATION

Deterministic intent extraction using rule-based parsing and configurable classifiers.

IDENTITY & INTENT
LATENCY: OPTIMIZED
DECISION

POLICY ENGINE

Evaluates requests against compiled logic bundles for deterministic decisions.

COMPILED LOGIC
LATENCY: OPTIMIZED
ENRICHMENT

CONTEXT ENGINE

Injects real-time enterprise data (roles, budgets, sensitivity) into decisions.

ENTERPRISE CONTEXT
LATENCY: OPTIMIZED
DISPATCH

ROUTING ENGINE

Securely forwards governed requests to LLM providers or internal tools.

GOVERNED PAYLOAD
LATENCY: OPTIMIZED

Deterministic Execution Control

Generative AI is inherently probabilistic — fundamentally unsafe for compliance, security routing, and authoritative governance.

In the iAgentic architecture, governance execution is fully deterministic.

The Enforcement Fabric extracts semantic intent and evaluates it against compiled, rule-based policies — decoupled from the generative model.

  • Provider-Agnostic Safety

    Policy outcomes must not depend on the specific nuances of an LLM's prompt adherence. Enforcement remains stable even if models are swapped.

  • Mathematical Reproducibility

    Given the same system state and policy version, the governance decision will always be exactly the same, guaranteed.

  • Immutable Lineage

    Every decision generates immutable evidence documenting the exact logic path taken, enabling perfect forensic reconstruction.

Policy Evaluation Contract

The iAgentic system operates on a deterministic evaluation contract. Every request is processed against a standardized schema to ensure predictable governance and auditability.

Input Parameters

intent— Normalized semantic objective extracted from the request
user_identity— Verified enterprise identity, role, and permissions
context— Environmental metadata (e.g., budget, time, location, system state)
risk_score— Security assessment based on request characteristics

Output Parameters

decision— ALLOW | DENY | REQUIRE_APPROVAL
policy_id— Identifier of the specific policy version applied
decision_reason— Deterministic justification for the system action

Failure Containment Flow

How iAgentic contains operational failures at each stage of execution.

The homepage shows the conceptual 5-step flow. This is the 8-stage operational containment path.

1

Request Initiated

User, agent, or copilot initiates an AI request

2

Gateway Intercept

Gateway intercepts request before execution

3

Intent Normalized

Intent and context are deterministically extracted

4

Policy Evaluated

Policy engine evaluates against compiled rules

5

Decision Returned

Allow, deny, or require approval

6

HITL Pauses

State machine pauses high-risk execution if required

7

Enforcement Acts

Fabric blocks, forwards, or resumes execution

8

Evidence Written

Immutable record written for audit reconstruction

Execution Paths

Synchronous Enforcement

Real-time decisioning for low-latency requirements. Requests are intercepted, evaluated, and enforced in-line with the system flow.

1
Ingress Gateway
2
Intent Normalization
3
Policy Evaluation
4
Enforcement Decision
5
Governed Dispatch
Immutable Audit Write

Asynchronous HITL

Approval workflows for high-risk decisions. The system pauses execution and routes the request for human review before proceeding.

POLICY TRIGGERED (HIGH RISK)
HITL Orchestrator
Resume Execution

Open Source Foundation

Built entirely on open-source infrastructure. No commercial license dependencies. No vendor lock-in.

Policy Engine

Apache 2.0

Declarative rule evaluation

Identity Provider

Apache 2.0

Enterprise OIDC with MFA

Secret Management

MPL 2.0

Encrypted vault with audit logging

AI Gateway

Apache 2.0

Multi-provider LLM routing and translation

Network Proxy

Apache 2.0

High-performance traffic interception

Observability

Apache 2.0

Distributed tracing and metrics

Architecture Roadmap

Available Today

  • •Distributed Enforcement Fabric for low-latency control
  • •Centralized Control Plane for policy orchestration
  • •Standardized Decision Schema (JSON)
  • •Stateful HITL state machine for high-risk flows

In Development

  • •Edge-based enforcement for global low-latency
  • •Federated policy management for multi-region clusters
  • •Native support for MCP (Model Context Protocol)
  • •Automated red-teaming and policy stress-testing