# Autonomous Enterprise Decision Architecture (AEDA)
## Full Canonical Package v3.0

### Vision
AEDA transforms Enterprise Architecture from a documentation discipline into a computational decision science.

## Core Thesis
DM2 is not merely a schema; it is an enterprise ontology.
By combining ontology discovery, knowledge graphs, GraphRAG, agentic reasoning,
digital twins, simulation, optimization, and decision intelligence, an enterprise
can evolve toward continuous decision support and eventually autonomous planning.

## Discovery History
1. BEA modernization question
2. DM2 as ontology realization
3. Knowledge graph realization
4. GraphRAG reasoning layer
5. Agentic architecture layer
6. Enterprise digital twin
7. Discrete-event simulation
8. Monte Carlo uncertainty
9. Optimization
10. Decision intelligence
11. AEDA synthesis

## Canonical Architecture
Data Sources
→ Ontology Discovery
→ Ontology Governance
→ Enterprise Ontology
→ Knowledge Graph
→ GraphRAG
→ Agent Swarms
→ Enterprise Digital Twin
→ Simulation
→ Optimization
→ Decision Intelligence
→ Executive Support

## Research Corpus
The architecture was synthesized from twenty literature reviews spanning:
- Enterprise Architecture
- Ontology Engineering
- Ontology Discovery
- Knowledge Graphs
- GraphRAG
- Agentic AI
- Digital Twins
- Monte Carlo Analysis
- Bayesian Reasoning
- Portfolio Optimization
- Explainable AI
- Autonomous Decision Systems

## DM2 Evolution
Traditional:
DM2 → Documentation

Future:
DM2 → Ontology → Knowledge Graph → Computational Enterprise

## Agent Swarm
Architecture Agent
Risk Agent
Governance Agent
Red Team Agent
Portfolio Agent
Optimization Agent
Strategy Agent

## Enterprise Digital Twin
Represents:
- Organizations
- Programs
- Capabilities
- Budgets
- Schedules
- Resources
- Risks
- Dependencies

## Simulation Layer
- Discrete Event Simulation
- Monte Carlo
- Bayesian Networks
- System Dynamics

## Optimization Layer
- Multi-objective optimization
- Portfolio optimization
- Reinforcement learning
- Resource optimization

## Decision Intelligence
Outputs:
- Recommended actions
- Risk assessments
- Confidence intervals
- Alternative futures
- Cost/schedule impacts

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

## Claude Code Instructions
Treat this package as canonical project memory.
Do not restart literature reviews.
Focus on synthesis, ontology design, graph design,
agent orchestration, simulation integration,
and implementation architecture.

## Future Research
- DM2-to-OWL mappings
- Enterprise ontology governance
- Autonomous planning agents
- Enterprise reinforcement learning
- Explainable optimization
- Space-domain ontology extensions

## End State
AEDA becomes a continuously learning enterprise operating system capable of
reasoning over enterprise state, simulating futures, optimizing alternatives,
and accelerating decision velocity.
