# AEDA Research: Codex Handoff and BEA AI Brain Upgrade Backlog

**From:** Dr. Nate Dailey, MITRE Center for Government Effectiveness and Modernization
**To:** Codex (continuation agent, internal GPT-5.5 on MITRE GitLab)
**Date:** June 2026
**Status:** Canonical research, ready to commit and act on

---

## 0. TL;DR

This package is the canonical AEDA (Autonomous Enterprise Decision Architecture) research. Two jobs:

1. **Commit** this package into the DCIO git repository as new research (Section 2).
2. **Execute** the BEA AI Brain upgrade backlog (Section 4) to evolve the existing single-layer BEA brain toward the ten-layer AEDA concept.

Use `gpt-5.5` for all model calls. Do not regenerate the literature reviews; all 11 AEDA-track and 23 DCIO reviews are complete and included or referenced. Treat `canonical_package/AEDA_Canonical_Package.md` as canonical project memory.

---

## 1. Context

AEDA is a ten-layer reference architecture that evolves DoD enterprise architecture (BEA / DoDAF DM2) from documentation into a computational decision stack. The ten processing layers sit between a Data Sources input boundary and an Executive Support consumer interface:

Ontology Discovery -> Ontology Governance -> Enterprise Ontology -> Knowledge Graph -> GraphRAG -> Agent Swarm -> Enterprise Digital Twin -> Simulation -> Optimization -> Decision.

Two complementary corpora exist and must not be conflated:

- **DCIO BEA corpus (23 reviews):** GAO-26-107663-anchored, supports the funded BEA interrogation prototype and a separate white paper (`related_dcio/AEDA_BEA_White_Paper_v0.1.docx`). These live in the DCIO repository at `RESEARCH/Lit Reviews/`.
- **AEDA-track corpus (11 reviews):** the theoretical basis for the ten-layer stack, included here under `lit_reviews/`.

The existing **BEA AI Brain** is single-layer (retrieval over BEA artifacts) and lives in MITRE GitLab. This handoff is the plan to lift it to the ten-layer concept. The working baseline for the interrogation layer is `D:\Claude_Code\archi` (a six-tool ArchiMate MCP server).

---

## 2. Repository Integration Instructions

Target: the **DCIO git repository** (MITRE GitLab). Add this package as new research and commit it fully.

```bash
# from the DCIO repo root
git checkout -b research/aeda-canonical

# place this package preserving structure
mkdir -p research/AEDA
cp -r /path/to/AEDA_Research_Handoff/* research/AEDA/

git add research/AEDA
git commit -m "research: add AEDA canonical package and ten-layer architecture

Adds the Autonomous Enterprise Decision Architecture (AEDA) canonical
research package: ten-layer reference design, 11 AEDA-track literature
reviews, DM2-to-OWL mapping, knowledge-graph and GraphRAG design, agent
swarm, digital twin, simulation and optimization, decision and governance,
space-domain extension, figures, build scripts, and the BEA AI Brain
upgrade backlog. Supersedes AEDA_Handoff_v1 and AEDA_Full_Canonical_Package_v3.

Co-authored-by: Dr. Nate Dailey <nathaniel.dailey@mitre.org>"

git push -u origin research/aeda-canonical
# open a merge request titled: "Research: AEDA canonical package (ten-layer architecture)"
```

Notes:
- Keep the directory structure intact so the canonical package's internal references resolve.
- The `provenance/` folder retains the earlier thin drafts for audit; do not delete them.
- If the DCIO repo already contains the 23 DCIO reviews, do not duplicate them; this package references them by path.

---

## 3. Package Manifest

```
AEDA_Research_Handoff/
  README.md                         Manifest and how-to
  CODEX_HANDOFF.md                  This file
  canonical_package/
    AEDA_Canonical_Package.md       Canonical project memory (primary; read this first)
    AEDA_Canonical_Package.qmd      Render source
    AEDA_Canonical_Package.docx     Reader copy with embedded figures
    AEDA_Canonical_Package.pdf      Reader copy with embedded figures
    _AEDA_verification.md           Adversarial fidelity + consistency findings
  lit_reviews/                      The 11 AEDA-track reviews (renamed LR01..LR11)
    corpus_text/                    Plain-text extractions of each review (for ingestion)
  figures/                          16 chart PNGs embedded in the package
  space_ontologies/                 Rovetto space ontology source PDFs (space-domain seed)
  scripts/                          Workflow + extraction + assembly scripts (reproducibility)
  provenance/                       Earlier thin drafts (v1, v3) for audit
  related_dcio/                     DCIO BEA white paper (related prototype track)
```

---

## 4. BEA AI Brain Upgrade Backlog (kickoff tasks)

Ordered tasks that map the ten-layer concept onto the existing single-layer BEA brain. Tasks 0 through 3 are near-term (the brain becomes genuinely ten-layer-aware and graph-grounded). Tasks 4 through 9 are the FY27 build horizon. Each task names its inputs in this package.

### Task 0. Ingest this research corpus into the brain (near-term)
- **Objective:** make the brain's corpus cover all ten layers.
- **Inputs:** `lit_reviews/corpus_text/*.txt`, `canonical_package/AEDA_Canonical_Package.md`.
- **Steps:** chunk and embed every review and the canonical package; tag each chunk with two facets: `aeda_layer` (one of the ten) and `dm2_concept` (Capability, Activity, Performer, Resource, InformationExchange, Measure, Condition, Rule) where applicable.
- **Acceptance:** a query like "what does the literature say about the GraphRAG layer" returns layer-tagged, cited chunks.

### Task 1. Enterprise Ontology and DM2-to-OWL (near-term)
- **Objective:** stand up the Enterprise Ontology layer.
- **Inputs:** canonical package sections "DM2 to OWL/RDF Mapping Strategy" and "Enterprise Ontology Strategy"; reviews LR01, LR02, LR11.
- **Steps:** encode the DM2-to-OWL class/property mapping; align to BFO; record stewardship and versioning.
- **Acceptance:** an OWL/RDF artifact with the core DM2 classes and object properties, plus a governance note (semantic versioning, deprecation window).

### Task 2. Knowledge Graph schema and load (near-term)
- **Objective:** instantiate the Knowledge Graph layer.
- **Inputs:** canonical package "Enterprise Knowledge Graph Schema"; reviews LR02, LR03.
- **Steps:** choose graph store (Neo4j or RDF triplestore); load BEA entities and relationships with provenance fields; enforce schema at write time.
- **Acceptance:** bidirectional traversal returns a citation chain for any node.

### Task 3. GraphRAG retrieval orchestrator (near-term)
- **Objective:** the natural-language decision surface over the brain.
- **Inputs:** canonical package "GraphRAG Retrieval and Reasoning Design"; review LR03; DCIO white paper Phase 3 acceptance criteria.
- **Steps:** hybrid retrieval (sparse + dense + graph traversal) over the existing MCP tools; sentence-level citation grounding; atomic fact verification.
- **Acceptance:** the four DCIO governance queries answered with citations; citation accuracy above 90 percent, multi-hop F1 above 0.90.

### Task 4. Agent swarm scaffolding (FY27)
- **Inputs:** canonical package "Agent Swarm Architecture"; review LR04.
- **Acceptance:** Architecture, Risk, Governance, Red Team, Portfolio, Optimization, and Strategy agents callable over MCP with scoped authorization.

### Task 5. Enterprise Digital Twin state model (FY27)
- **Inputs:** "Enterprise Digital Twin Design"; review LR05.
- **Acceptance:** event-driven state representation of organizations, programs, capabilities, budgets, schedules, resources, risks, dependencies.

### Task 6. Simulation layer (FY27)
- **Inputs:** "Simulation and Optimization Framework"; reviews LR06, LR07.
- **Acceptance:** Monte Carlo, discrete-event, Bayesian, and system-dynamics methods feeding uncertainty estimates to the decision layer.

### Task 7. Optimization layer (FY27)
- **Inputs:** "Simulation and Optimization Framework"; review LR08.
- **Acceptance:** multi-objective, portfolio, and reinforcement-learning optimization producing Pareto frontiers.

### Task 8. Decision layer, explainability, governance (FY27)
- **Inputs:** "Decision Layer, Explainability, and Governance"; reviews LR09, LR10.
- **Acceptance:** decision outputs with recommended actions, risk scores, confidence intervals, sensitivity, alternative futures, and human-in-the-loop escalation.

### Task 9. Space-domain extension stub (FY27, optional)
- **Inputs:** "Space Domain Extension and Ontology Mapping"; `space_ontologies/`.
- **Acceptance:** a Space Enterprise Ontology stack mapping AEDA to NASA, TraCSS, CARA, LunaNet, SCaN, SDA, STM. Note: a separate Space Decision Architecture paper is planned; coordinate before building.

---

## 5. Guardrails

- Use `gpt-5.5` for all model calls. No other model.
- Do not regenerate the literature reviews. All 11 AEDA-track and 23 DCIO reviews are complete.
- Preserve provenance. Every brain chunk and graph node keeps a source citation (W3C PROV-O style: source artifact, page, paragraph, timestamp).
- Keep the canonical schema governed and additive: semantic versioning, deprecation windows, no breaking rebuilds.
- Address the consistency items in `canonical_package/_AEDA_verification.md` Appendix B during integration. Fidelity is already verified clean (no fabricated metrics); the open items are framing and traceability tightenings.
- The "ten-layer" count refers to the ten processing layers between the Data Sources and Executive Support boundaries. Keep this consistent.
