# Autonomous Enterprise Decision Architecture (AEDA)

## Canonical Research Package, Architecture Vision, and White Paper Foundation

Version 1.0 --- June 2026

# Executive Summary

This document serves as the canonical handoff package for the Autonomous
Enterprise Decision Architecture (AEDA) initiative.

It captures:

-   The complete evolution of the concept
-   Research discoveries
-   Architectural synthesis
-   White paper foundation
-   Ontology strategy
-   Enterprise Architecture integration
-   Knowledge Graph strategy
-   GraphRAG strategy
-   Agentic AI strategy
-   Enterprise Digital Twin strategy
-   Simulation and Optimization strategy
-   Decision Intelligence strategy
-   Future implementation roadmap

# The Discovery Journey

## Initial Question

Can Enterprise Architecture become AI-native?

The original context focused on: - BEA - DoDAF - DM2

Research evolved through:

Enterprise Architecture → Enterprise Ontologies → Knowledge Graphs →
GraphRAG → Agentic Systems → Digital Twins → Simulation → Optimization →
Decision Intelligence → Autonomous Enterprise

# Final Synthesis

Ontology Discovery → Enterprise Ontology → Knowledge Graph → GraphRAG →
Agent Swarms → Digital Twin → Simulation → Optimization → Decision
Intelligence → Autonomous Enterprise

This architecture was named:

# Autonomous Enterprise Decision Architecture (AEDA)

# Canonical Definitions

## Enterprise Ontology

A formal machine-readable representation of enterprise concepts,
relationships, constraints, and semantics.

## Enterprise Knowledge Graph

A graph representation of enterprise knowledge instantiated from
enterprise ontologies.

## GraphRAG

A retrieval and reasoning architecture combining graph traversal, vector
retrieval, semantic reasoning, and evidence generation.

## Agent Swarm

A coordinated collection of specialized reasoning agents.

## Enterprise Digital Twin

A continuously updated computational representation of enterprise state
and behavior.

## Decision Intelligence

The discipline of combining analytics, AI, simulation, and optimization
to improve decisions.

## AEDA

A semantic, ontology-driven architecture that continuously observes,
reasons, predicts, optimizes, and recommends enterprise decisions.

# Canonical Architecture

Enterprise Data Sources → Ontology Discovery Layer → Ontology Governance
Layer → Enterprise Ontology → Knowledge Graph Layer → GraphRAG Layer →
Agentic Reasoning Layer → Enterprise Digital Twin → Simulation Layer →
Optimization Layer → Decision Intelligence Layer → Executive Decision
Support

# Enterprise Ontology Strategy

Foundation Ontologies (BFO, DOLCE, SUMO) → Enterprise Ontologies (DM2,
UAF, BEA) → Domain Ontologies → Mission Ontologies → Program Ontologies
→ Operational Ontologies

# White Paper Thesis

Traditional Enterprise Architecture answers:

"What exists?"

AEDA answers:

-   What exists?
-   What is happening?
-   What will happen?
-   What should we do?
-   What are the risks?
-   What alternative futures exist?

# Implementation Roadmap

1.  Ontology Discovery
2.  Enterprise Ontology
3.  Knowledge Graph
4.  GraphRAG
5.  Agent Swarms
6.  Digital Twin
7.  Simulation
8.  Optimization
9.  Decision Intelligence
10. Autonomous Enterprise

# Claude Code Instructions

Assume: - Literature review phase complete - Research corpus
established - Architecture concept established

Prioritize: 1. Ontology design 2. DM2 mapping 3. Knowledge graph schema
4. GraphRAG implementation 5. Agent orchestration 6. Digital twin
architecture 7. Simulation integration 8. Optimization frameworks 9.
Decision intelligence

Primary objective:

Build the first implementation-ready specification of the Autonomous
Enterprise Decision Architecture (AEDA).
